Salivary transcriptomic and microbial biomarkers for pancreatic cancer

ABSTRACT

The present invention relates to the identification of pancreatic cancer biomarkers for the detection of early pancreatic cancer. The present invention also provides methods of diagnosing pancreatic cancer and distinguishing between pancreatic cancer and chronic pancreatitis. The present invention additionally provides kits that find use in the practice of the methods of the invention.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/220,482, filed on Jun. 25, 2009, which is incorporated herein byreference in its entirety.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH OR DEVELOPMENT

This invention was made with Government support of Grant No. U01DE016275, awarded by the National Institutes of Health. The Governmenthas certain rights in this invention.

REFERENCE TO A “SEQUENCE LISTING,” A TABLE, OR A COMPUTER PROGRAMLISTING APPENDIX SUBMITTED ON A COMPACT DISK

The sequence listing contained in the file named “008074-5029 SequenceListing.txt”, created on Jul. 30, 2010 and having a size of 37.9kilobytes, has been submitted electronically herewith via EFS-Web, andthe contents of the txt file are hereby incorporated by reference intheir entirety.

BACKGROUND OF THE INVENTION

Pancreatic cancer is a disease that accounts for an estimated 43,000cases and 36,000 deaths in the United States every year and is the4^(th) leading cause of cancer deaths among both men and women in theUnited States. Ductal adenocarcinomas are the most common form ofpancreatic cancer, and about 80% of adenocarcinomas occur at the head ofthe pancreas, where they frequently cause obstructive jaundice (ablockage of the bile ducts). Pancreatic cancer can also cause severeupper abdominal pain; weight loss; splenic vein obstruction, resultingin splenomegaly, gastric and esophageal varices, and GI hemorrhage; anddiabetes.

In over 90% of patients with pancreatic cancer, diagnosis does not occuruntil a late stage of cancer, because the disease is usuallyasymptomatic at early stages. Frequently, by the time patients presentwith symptoms of pancreatic cancer and are diagnosed with the disease,the cancer has spread to regional lymph nodes or metastasized to theliver or lung. Although the prognosis for patients diagnosed withpancreatic cancer varies with stage, the overall prognosis for thedisease is poor, and less than 4% of all patients survive longer than 5years, due in part to the fact that patients generally have an advancedstage of the disease at the time of diagnosis. About 80-90% ofpancreatic cancers are surgically unresectable by the time of diagnosisbecause of metastasis or invasion of blood vessels. Therefore, thetreatment options that are available for most pancreatic cancer patientsare more limited than they would be if earlier detection of pancreaticcancer were available. Thus, detection of pancreatic cancer at an earlystage would likely improve the mortality rates associated with thisdisease¹⁻³.

Biomarkers are measurable biological and physiological parameters thatcan serve as indices for health-related assessments, such as diagnosisof disease. Nucleic acid and protein biomarkers are especially usefulbecause they are amenable to bodily fluid tests (such as saliva tests),which are easier and more convenient to administer than tissue biopsytests. With respect to detecting early stage pancreatic cancer, however,the current biomarkers and testing strategies that exist^(1,4-8) arelimited in their usefulness because they are either confined to a smallnumber of patients at greater risk, rely on invasive procedures, or lackthe necessary sensitivity and specificity to be suitable for widespreadscreening⁹⁻¹¹. For example, pancreas-associated antigen CA 19-9 is usedto screen patients who are at high risk of developing pancreatic cancer,but it is not sensitive or specific enough to be suitable for generalpopulation screening. Additionally, the search for potential usefulbiomarkers of pancreatic cancer is further complicated by the existenceof several benign pancreatic diseases, such as chronic pancreatitis,which has phenotypic overlap with early stage pancreatic cancer. Inparticular, the lack of specificity of currently used pancreatic cancerbiomarkers is often due to the presence of these biomarkers in patientswith chronic pancreatitis^(4,5).

Saliva has gained attention as a diagnostic fluid because it is simpleto collect and readily accessible via a non-invasive procedure. Salivaryconstituents including DNA, RNA, protein, and bacteria have been studiedas potential diagnostic markers for various diseases, such as oraldisease^(15,16 17 18) and systemic disease¹⁹ ²⁰ ²¹ ²².

Given the importance of early detection for successful treatment ofpancreatic cancer, and given the limitations of the currently existingstrategies for detecting pancreatic cancer at an early stage, there is aneed in the field for biomarkers of pancreatic cancer, and methods ofusing such biomarkers, that are minimally invasive and also sensitiveand specific enough in detecting early-stage pancreatic cancer to besuitable for widespread screening of patients. The present inventionaddresses this need and others.

BRIEF SUMMARY OF THE INVENTION

In one aspect, the present invention provides a method of diagnosingpancreatic cancer in a subject, the method comprising the steps of (a)analyzing a saliva sample from the subject with an assay thatspecifically detects a marker selected from the group consisting of anucleic acid or polypeptide encoded by a nucleic acid listed in FIG. 4;and (b) comparing the level of expression of the marker to a control todetermine whether or not the marker is differentially expressed in thesample as compared to the control; thereby providing a diagnosis forpancreatic cancer.

In one embodiment, the analyzing step comprises analyzing the salivasample from the subject with an assay that specifically detects themarkers KRAS, MBD3L2, ACRV1, and DPM1, and wherein the comparing stepcomprises determining whether or not KRAS, MBD3L2, ACRV1, and DPM1 aredifferentially expressed in the sample. In one embodiment, the markerdistinguishes between chronic pancreatitis and pancreatic cancer. In oneembodiment, the markers are CDKL, MBD3L2, and KRAS and they are alldetected.

In another aspect, the present invention provides a method of diagnosingpancreatic cancer in a subject, the method comprising the steps of: (a)analyzing a saliva sample from the subject with an assay thatspecifically detects a marker selected from the group consisting of amicrobe listed in FIG. 5; and (b) comparing the amount of the marker toa control to determine whether or not the marker is increased ordecreased in the sample as compared to the control; thereby providing adiagnosis for pancreatic cancer.

In one embodiment, the analyzing step comprises analyzing the salivasample from the subject with an assay that specifically detectsNeisseria elongate and Streptococcus mitis or Granulicatella adiacensand Streptococcus mitis, and wherein the comparing step comprisesdetermining whether or not the levels of Neisseria elongate andStreptococcus mitis or Granulicatella adiacens and Streptococcus mitishave increased or decreased in the sample relative to a control. In oneembodiment, the marker distinguishes between chronic pancreatitis andpancreatic cancer. In one embodiment, the markers are Granulicatellaadiacens and Streptococcus mitis and they are both detected.

In one embodiment, the assay detects more than one marker. In oneembodiment, the assay detects protein and is ELISA, Western blotting,flow cytometry, immunofluorescence, immunohistochemistry, or massspectroscopy. In one embodiment, the assay detects nucleic acid and ismass spectroscopy, PCR, microarray hybridization, thermal cyclesequencing, capillary array sequencing, or solid phase sequencing.

In one embodiment, the assay comprises a reagent that binds to aprotein. In one embodiment, the reagent is an antibody. In oneembodiment, the reagent is a monoclonal antibody. In one embodiment, theassay comprises a reagent that binds to a protein and the assay isELISA, Western blotting, flow cytometry, immunofluorescence,immunohistochemistry, or mass spectroscopy.

In one embodiment, the assay comprises a reagent that binds to a nucleicacid. In one embodiment, the reagent is a nucleic acid. In oneembodiment, the reagent is an oligonucleotide. In one embodiment, thereagent is an RT-PCR primer set.

In yet another aspect, the present invention provides a kit fordiagnosing pancreatic cancer in a subject, the kit comprising a reagentthat specifically detects a marker selected from the group consisting ofa nucleic acid or polypeptide encoded by a nucleic acid listed in FIG.4.

In one embodiment, the kit comprises reagents that specifically detectthe markers KRAS, MBD3L2, ACRV1, and DPM1.

In still another aspect, the present invention provides a kit fordiagnosing pancreatic cancer in a subject, the kit comprising a reagentthat specifically detects marker selected from the group consisting of amicrobe listed in FIG. 5.

In one embodiment, the kit comprises reagents that specifically detectthe markers Granulicatella adiacens and Streptococcus mitis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic of the strategy used for the discovery and validationof salivary biomarkers.

FIG. 2. ROC curve and interactive dot diagram for the logisticregression model. A. The logistic regression model using four biomarkers(KRAS, MBD3L2, ACRV1, and DPM1) yielded an AUC value of 0.971 (cutoff0.433). B. Interactive dot diagram was based on the qPCR data of thenon-cancer group (n=60) and cancer group (n=30).

FIG. 3. Demographic information (age, gender, ethnicity, smokinghistory, and drinking history) for subjects in the discovery andvalidation phases. For the validation samples, p-value was calculatedamong three groups. Detailed information on individual characteristicsis presented in Table A1.

FIG. 4. Quantitative PCR results of eleven validated mRNA biomarkers insaliva. Quantitative PCR was used to validate the microarray findings onan independent clinical sample set, including saliva from 30 pancreaticcancer patients, 30 healthy control subjects, and 30 chronicpancreatitis patients. Wilcoxon Signed Rank test: if P<0.05, the markeris validated. “↑” (upwards-facing arrow): upregulated in pancreaticcancer; “↓,” (downwards-facing arrow): downregulated in pancreaticcancer.

FIG. 5. Quantitative PCR results of six confirmed bacterial biomarkersin saliva pellet (n=83). Quantitative PCR was performed to validate theHOMIM microarray findings on an independent clinical sample set,including saliva from 28 pancreatic cancer patients (Pc), 28 healthycontrol subjects (normal), and 27 chronic pancreatitis patients (Pt).Wilcoxon Signed Rank test: if P<0.05, the marker is validated. “↑”(upwards-facing arrow): upregulated in pancreatic cancer; “↓,”(downwards-facing arrow): downregulated in pancreatic cancer. Foldchange is only shown for the validated biomarkers.

FIG. 6. Combination of salivary biomarkers for pancreatic cancerselected by logistic regression model. The logistic regression model wasbuilt based on the validated mRNA biomarkers or validated bacterialbiomarkers for distinguishing pancreatic cancer from healthy controls,pancreatic cancer from chronic pancreatitis, and pancreatic cancer fromthe non-cancer group. The best models for each comparison, providing thehighest discriminatory power with the simplest combination, are shownwith the symbol of each biomarkers. Abbreviations: 95% CI=95% confidenceinterval; P=significance level P (area=0.5).

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention relates to the discovery of novel nucleic acid andmicrobial pancreatic cancer biomarkers in saliva and novel methods ofusing such biomarkers for diagnosing pancreatic cancer. The presentinvention also provides kits useful in the practice of the methods ofthe invention.

Pancreatic cancer generally has a poor prognosis, with a five-year lifeexpectancy of less than about 4%, due in part to the late onset ofsymptoms of pancreatic cancer, frequently after the cancer hasmetastasized or become surgically unresectable. Therefore, methods ofdiagnosing pancreatic cancer at an early stage, prior to the point inwhich the cancer has metastasized or become surgically unresectable,would likely lead to improved outcomes and higher life expectancy ratesfor individuals having pancreatic cancer. However, the methods ofdiagnosing pancreatic cancer should be specific enough to distinguishbetween pancreatic cancer and chronic pancreatitis, a chronicinflammation of the pancreas that is a risk factor for later developingpancreatic cancer, but which is not cancer and does not always lead tocancer.

We performed high-throughput analyses of the salivary transcriptome andsalivary microbial profiles of samples from individuals with pancreaticcancer, individuals with chronic pancreatitis, and healthy controls. Theresults of these initial screens were verified by quantitative PCR, andverified candidates were subsequently independently validated byquantitative PCR, to yield twelve novel mRNA biomarkers of pancreaticcancer (including 7 genes that are upregulated in pancreatic cancer and5 genes that are downregulated in pancreatic cancer) and three microbialmarkers of pancreatic cancer (including 2 microbes that aredownregulated in pancreatic cancer and one microbe that is upregulatedin pancreatic cancer).

To test the prediction/classification power of these biomarkers,logistic model and receiver operating characteristic curve (ROC)analysis were performed based on the validated results. A combination offour mRNA biomarkers (KRAS, MBD3L2, ACRV1, and CDKL3) is highlysensitive and specific for distinguishing pancreatic cancer patientsfrom healthy subjects (ROC-plot area under the curve (AUC) value of0.973; 93.3% sensitivity; 100% specificity), as is a combination of twobacterial biomarkers (N. elongata and S. mitis) (ROC-plot AUC value of0.895; 96.4% sensitivity; 82.1% specificity). Additionally, thecombination of mRNA biomarkers KRAS, MBD3L2, and CDKL3 is highlysensitive and specific for distinguishing pancreatic cancer patientsfrom patients with chronic pancreatitis (ROC-plot AUC value of 0.981;96.7% sensitivity; 96.7% specificity). Furthermore, a four-markerlogistic regression model using the mRNA biomarkers KRAS, MBD3L2, ACRV1,and CDKL3 provided the highest discriminatory power for differentiatingpancreatic cancer subjects from non-cancer subjects (ROC-plot AUC valueof 0.971, 90.0% sensitivity, 95.0% specificity using a cutoff of 0.433).Thus, our results provide novel biomarkers and combinations ofbiomarkers for the detection of pancreatic cancer.

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The terms “biomarker” or “pancreatic cancer biomarker” interchangeablyrefer to a gene, mRNA, protein, or microbe that is present in abiological sample, e.g. saliva, from a subject with a disease, such aspancreatic cancer, at a different level or concentration in comparisonto a biological sample from a subject without the disease, and which isuseful for the diagnosis of the disease, for providing a prognosis, orfor preferential targeting of a pharmacological agent to an affectedcell or tissue.

Pancreatic cancer biomarkers recited herein refer to polypeptides,nucleic acids, and microbes, e.g., gene, pre-mRNA, mRNA, polymorphicvariants, alleles, mutants, and interspecies homologs that: (1) have anamino acid sequence that has greater than about 60% amino acid sequenceidentity, 65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%,95%, 96%, 97%, 98% or 99% or greater amino acid sequence identity,preferably over a region of over a region of at least about 25, 50, 100,200, 500, 1000, or more amino acids, to a polypeptide encoded by areferenced nucleic acid or an amino acid sequence described herein; (2)specifically bind to antibodies, e.g., polyclonal antibodies, raisedagainst an immunogen comprising a referenced amino acid sequence,immunogenic fragments thereof, and conservatively modified variantsthereof; (3) specifically hybridize under stringent hybridizationconditions to a nucleic acid encoding a referenced amino acid sequence,and conservatively modified variants thereof; or (4) have a nucleic acidsequence that has greater than about 60% nucleotide sequence identity,65%, 70%, 75%, 80%, 85%, 90%, preferably 91%, 92%, 93%, 94%, 95%, 96%,97%, 98% or 99% or higher nucleotide sequence identity, preferably overa region of at least about 10, 15, 20, 25, 50, 100, 200, 500, 1000, ormore nucleotides, to a reference nucleic acid sequence. A polynucleotideor polypeptide sequence is typically from a mammal including, but notlimited to, primate, e.g., human; rodent, e.g., rat, mouse, hamster;cow, pig, horse, sheep, or any mammal. The nucleic acids and proteins ofthe invention include both naturally occurring or recombinant molecules.Truncated and alternatively spliced forms of these antigens are includedin the definition.

Biomarkers of the invention may be identified by gene name, e.g.,v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog; gene symbol, e.g.,KRAS; locus in the human genome, e.g. 12p12.1; Genbank accession number,e.g., NM_(—)004985; or the like. It is understood that all of theseidentifiers reference the same biomarker and thus are equivalent.Salivary transcriptome biomarkers of the invention are identified inTable 4 below, and include: ACRV1 (acrosomal vesicle protein 1;NM_(—)001612); CDC14B (CDC14 cell division cycle 14 homolog B;NM_(—)003671); ASH2L (ash2-like; NM_(—)004674); STIM2 (stromalinteraction molecule 2; NM_(—)020860); GPR124 (G protein-coupledreceptor 124; NM_(—)032777); LILRA2 (leukocyte immunoglobulin-likereceptor, subfamily A, member 2; NM_(—)006866); ENG (endoglin;NM_(—)000118); RMB24 (RNA binding motif protein 24; NM_(—)153020); LRRK1(leucine-rich repeat kinase 1; NM_(—)024652); DMXL2 (Dmx-like 2;NM_(—)015263); ZSCAN16 (zinc finger and SCAN domain containing 16;NM_(—)025231); MBD3L2 (methyl-CpG binding domain protein 3-like 2;NM_(—)144614); GPX3 (glutathione peroxidase 3; NM_(—)002084); ITGA2B(integrin, alpha 2b; NM_(—)000419); CDH4 (cadherin 4; NM_(—)001794);S100P (S100 calcium binding protein P; NM_(—)005980); FTHP1 (ferritin,heavy polypeptide pseudogene 1; NG_(—)005639); ZMIZ2 (zinc finger,MIZ-type containing 2; NM 031449); DDX3X (DEAD (Asp-Glu-Ala-Asp; SEQ IDNO: 161) box polypeptide 3, X-linked; NM_(—)001356); UTF1(undifferentiated embryonic cell transcription factor 1; NM_(—)003577);KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog;NM_(—)004985); DMD (dystrophin; NM_(—)000109); CABLES1 (Cdk5 and Ablenzyme substrate 1; NM_(—)001100619); TPT1 (tumor protein,translationally-controlled 1; NM_(—)003295); MARCKS (myristoylatedalanine-rich protein kinase C substrate; NM_(—)002356); SAT1(spermidine/spermine N1-acetyltransferase 1; NM_(—)002970); PNPLA8(patatin-like phospholipase domain containing 8; NM_(—)015723); DPM1(dolichyl-phosphate mannosyltransferase polypeptide 1; NM_(—)003859);CD7 (CD7 molecule; NM_(—)006137); PCSK6 (proprotein convertasesubtilisin/kexin type 6; NM_(—)138319); TK2 (thymidine kinase 2;NM_(—)004614); FTH1 (ferritin, heavy polypeptide 1; NM_(—)002032);TUBA1A (tubulin, alpha 1b; NM_(—)006082); GLTSCR2 (glioma tumorsuppressor candidate region gene 2; NM_(—)0015710); and CDKL3(cyclin-dependent kinase-like 3; NM_(—)016508).

Alternatively, biomarkers of the invention may be identified by the nameof a “microbe,” or microscopic unicellular organism. Microbialbiomarkers of the invention are identified in FIG. 5, and include:Atopobium parvulum (A. parvulum), Granulicatella adiacens (G. adiacens),Neisseria elongata (N. elongata), Prevotella nigrescens (P. nigrescens),Streptococcus australis (S. australis), and Streptococcus mitis (S.mitis).

The term “cancer” refers to human cancers and carcinomas, sarcomas,adenocarcinomas, lymphomas, leukemias, solid and lymphoid cancers, etc.Examples of different types of cancer include, but are not limited to,pancreatic cancer, breast cancer, gastric cancer, bladder cancer, oralcancer, ovarian cancer, thyroid cancer, lung cancer, prostate cancer,uterine cancer, testicular cancer, neuroblastoma, squamous cellcarcinoma of the head, neck, cervix and vagina, multiple myeloma, softtissue and osteogenic sarcoma, colorectal cancer, liver cancer (i.e.,hepatocarcinoma), renal cancer (i.e., renal cell carcinoma), pleuralcancer, cervical cancer, anal cancer, bile duct cancer, gastrointestinalcarcinoid tumors, esophageal cancer, gall bladder cancer, smallintestine cancer, cancer of the central nervous system, skin cancer,choriocarcinoma; osteogenic sarcoma, fibrosarcoma, glioma, melanoma,B-cell lymphoma, non-Hodgkin's lymphoma, Burkitt's lymphoma, Small Celllymphoma, Large Cell lymphoma, monocytic leukemia, myelogenous leukemia,acute lymphocytic leukemia, and acute myelocytic leukemia. Cancersembraced in the current application include both metastatic andnon-metastatic cancers.

As used herein, “pancreatic cancer” refers to a group of malignant orneoplastic cancers originating in the pancreas of an individual.Non-limiting examples of pancreatic cancers include adenocarcinomas(e.g., ductal adenocarcinoma and acinar cell adenocarcinoma),adenosquamous carcinomas, squamous cell carcinomas, giant cellcarcinomas, cystadenocarcinomas, and pancreatic neuroendocrinecarcinomas.

“Metastasis” refers to spread of a cancer from the primary tumor ororigin to other tissues and parts of the body, such as the lymph nodes.

“Saliva” refers to any watery discharge from the mouth, nose, or throat.For the purposes of this invention, saliva may include sputum and nasalor post nasal mucous.

“Diagnosis” refers to identification of a disease state, such as canceror chronic pancreatitis, in a subject. The methods of diagnosis providedby the present invention can be combined with other methods of diagnosiswell known in the art. Non-limiting examples of other methods ofdiagnosis include, detection of known disease biomarkers in salivasamples, oral radiography, co-axial tomography (CAT) scans, positronemission tomography (PET), radionuclide scanning, oral biopsy, and thelike.

“Providing a prognosis” refers to providing a prediction of thelikelihood of metastasis, predictions of disease free and overallsurvival, the probable course and outcome of cancer therapy, or thelikelihood of recovery from the cancer, in a subject.

The term “differentially expressed” or “differentially regulated” refersgenerally to a protein or nucleic acid that is overexpressed(upregulated) or underexpressed (downregulated) in one biological samplecompared to at least one other sample, generally in saliva from asubject with cancer or a cancer cell, in comparison to saliva from asubject without cancer or a non-cancer cell, in the context of thepresent invention.

The terms “overexpress,” “overexpression,” “overexpressed,”“upregulate,” or “upregulated” interchangeably refer to a biomarker thatis present at a detectably greater level in a biological sample, e.g.saliva or cancer cell, from a patient with cancer, in comparison to abiological sample from a patient without cancer. The term includesoverexpression in a sample from a patient with cancer due totranscription, post transcriptional processing, translation,post-translational processing, cellular localization (e.g, organelle,cytoplasm, nucleus, cell surface), and RNA and protein stability, ascompared to a sample from a patient without cancer. Overexpression canbe 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more in comparison toa sample from a patient without cancer. In certain instances,overexpression is 1-fold, 2-fold, 3-fold, 4-fold 5, 6, 7, 8, 9, 10, or15-fold or more higher levels of transcription, translation, or microbepresence in comparison to a sample from a patient without cancer.

The terms “underexpress,” “underexpression,” “underexpressed,”“downregulate,” or “downregulated” interchangeably refer to a biomarkerthat is present at a detectably lower level in a biological sample, e.g.saliva or cancer cell, in comparison to a biological sample from asubject without cancer. The term includes underexpression due totranscription, post transcriptional processing, translation,post-translational processing, cellular localization (e.g., organelle,cytoplasm, nucleus, cell surface), and RNA and protein stability, ascompared to a control. Underexpression can be 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, 90% or less in comparison to a sample from a subjectwithout cancer. In certain instances, underexpression is 1-fold, 2-fold,3-fold, 4-fold or more lower levels of transcription, translation ormicrobe presence in comparison to a control. Overexpression andunderexpression can be detected using conventional techniques fordetecting mRNA (e.g., RT-PCR, PCR, hybridization), proteins (e.g.,ELISA, immunohistochemical techniques, mass spectroscopy, Luminex® xMAPtechnology), or microbes (e.g., microbial nucleic acid profiling).

It will be understood by the skilled artisan that markers may be usedsingly or in combination with other markers for any of the uses, e.g.,diagnosis or prognosis of pancreatic cancer.

“Disease transcriptome,” “pancreatic cancer transcriptome,” or “salivarypancreatic transcriptome” refers to a set of genes differentiallyexpressed in a biological sample from an individual or group ofindividuals suffering from a given disease. Disease transcriptomes maybe derived from a particular biological sample, i.e. saliva as in thescope of the present invention. Many disease transcriptomes are known inthe art, as are methods of determining a disease transcriptome (see,e.g., U.S. Pat. Nos. 7,229,774, 7,378,239, 7,378,236, 6,833,247, and7,171,311).

As used herein, an “expression profile” refers to the quantitative orqualitative level of a biomarker found in a transcriptome, such as acontrol or salivary pancreatic cancer transcriptome, or the quantitativeor qualitative level of a microbial biomarker. A salivary pancreaticcancer expression profile may comprise, for example, the quantitative orqualitative level of nucleic acid or protein of one or moretranscriptome and/or microbial biomarkers that are differentiallyexpressed in the saliva of an individual having pancreatic cancer ascompared to an individual who does not have pancreatic cancer (e.g., ahealthy control, a non-cancer control, and/or a control having chronicpancreatitis).

“Biological sample” includes sections of tissues such as biopsy andautopsy samples, and frozen sections taken for histologic purposes. Suchsamples include blood and blood fractions or products (e.g., serum,plasma, platelets, red blood cells, and the like), sputum or saliva,lymph and tongue tissue, cultured cells, e.g., primary cultures,explants, and transformed cells, stool, urine, etc. A biological sampleis typically obtained from a eukaryotic organism, most preferably amammal such as a primate e.g., chimpanzee or human; cow; dog; cat; arodent, e.g., guinea pig, rat, or mouse; rabbit; or a bird; reptile; orfish.

The terms “identical” or percent “identity,” in the context of two ormore nucleic acids or polypeptide sequences, refer to two or moresequences or subsequences that are the same or have a specifiedpercentage of amino acid residues or nucleotides that are the same(i.e., about 60% identity, preferably 65%, 70%, 75%, 80%, 85%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher identity over aspecified region, when compared and aligned for maximum correspondenceover a comparison window or designated region) as measured using a BLASTor BLAST 2.0 sequence comparison algorithms with default parametersdescribed below, or by manual alignment and visual inspection (see,e.g., NCBI web site http://www.ncbi.nlm.nih.gov/BLAST/ or the like).Such sequences are then said to be “substantially identical.” Thisdefinition also refers to, or may be applied to, the compliment of atest sequence. The definition also includes sequences that havedeletions and/or additions, as well as those that have substitutions. Asdescribed below, the preferred algorithms can account for gaps and thelike. Preferably, identity exists over a region that is at least about25 amino acids or nucleotides in length, or more preferably over aregion that is 50-100 amino acids or nucleotides in length.

For sequence comparison, typically one sequence acts as a referencesequence, to which test sequences are compared. When using a sequencecomparison algorithm, test and reference sequences are entered into acomputer, subsequence coordinates are designated, if necessary, andsequence algorithm program parameters are designated. Preferably,default program parameters can be used, or alternative parameters can bedesignated. The sequence comparison algorithm then calculates thepercent sequence identities for the test sequences relative to thereference sequence, based on the program parameters.

A “comparison window,” as used herein, includes reference to a segmentof any one of the number of contiguous positions selected from the groupconsisting of from 20 to 600, usually about 50 to about 200, moreusually about 100 to about 150 in which a sequence may be compared to areference sequence of the same number of contiguous positions after thetwo sequences are optimally aligned. Methods of alignment of sequencesfor comparison are well-known in the art. Optimal alignment of sequencesfor comparison can be conducted, e.g., by the local homology algorithmof Smith & Waterman, Adv. Appl. Math., 2:482 (1981), by the homologyalignment algorithm of Needleman & Wunsch, J. Mol. Biol., 48:443 (1970),by the search for similarity method of Pearson & Lipman, Proc. Nat'l.Acad. Sci. USA, 85:2444 (1988), by computerized implementations of thesealgorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin GeneticsSoftware Package, Genetics Computer Group, 575 Science Dr., Madison,Wis.), or by manual alignment and visual inspection (see, e.g., CurrentProtocols in Molecular Biology (Ausubel et al., eds. 1987-2005, WileyInterscience)).

A preferred example of algorithm that is suitable for determiningpercent sequence identity and sequence similarity are the BLAST andBLAST 2.0 algorithms, which are described in Altschul et al., Nuc. AcidsRes., 25:3389-3402 (1977) and Altschul et al., J. Mol. Biol.,215:403-410 (1990), respectively. BLAST and BLAST 2.0 are used, with theparameters described herein, to determine percent sequence identity forthe nucleic acids and proteins of the invention. Software for performingBLAST analyses is publicly available through the National Center forBiotechnology Information (http://www.ncbi.nlm.nih.gov/). This algorithminvolves first identifying high scoring sequence pairs (HSPs) byidentifying short words of length W in the query sequence, which eithermatch or satisfy some positive-valued threshold score T when alignedwith a word of the same length in a database sequence. T is referred toas the neighborhood word score threshold (Altschul et al., supra). Theseinitial neighborhood word hits act as seeds for initiating searches tofind longer HSPs containing them. The word hits are extended in bothdirections along each sequence for as far as the cumulative alignmentscore can be increased. Cumulative scores are calculated using, fornucleotide sequences, the parameters M (reward score for a pair ofmatching residues; always >0) and N (penalty score for mismatchingresidues; always <0). For amino acid sequences, a scoring matrix is usedto calculate the cumulative score. Extension of the word hits in eachdirection are halted when: the cumulative alignment score falls off bythe quantity X from its maximum achieved value; the cumulative scoregoes to zero or below, due to the accumulation of one or morenegative-scoring residue alignments; or the end of either sequence isreached. The BLAST algorithm parameters W, T, and X determine thesensitivity and speed of the alignment. The BLASTN program (fornucleotide sequences) uses as defaults a wordlength (W) of 11, anexpectation (E) of 10, M=5, N=−4 and a comparison of both strands. Foramino acid sequences, the BLASTP program uses as defaults a wordlengthof 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (seeHenikoff & Henikoff, Proc. Natl. Acad. Sci. USA, 89:10915 (1989))alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparisonof both strands.

“Nucleic acid” refers to deoxyribonucleotides or ribonucleotides andpolymers thereof in either single- or double-stranded form, andcomplements thereof. The term encompasses nucleic acids containing knownnucleotide analogs or modified backbone residues or linkages, which aresynthetic, naturally occurring, and non-naturally occurring, which havesimilar binding properties as the reference nucleic acid, and which aremetabolized in a manner similar to the reference nucleotides. Examplesof such analogs include, without limitation, phosphorothioates,phosphoramidates, methyl phosphonates, chiral-methyl phosphonates,2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs).

Unless otherwise indicated, a particular nucleic acid sequence alsoimplicitly encompasses conservatively modified variants thereof (e.g.,degenerate codon substitutions) and complementary sequences, as well asthe sequence explicitly indicated. Specifically, degenerate codonsubstitutions may be achieved by generating sequences in which the thirdposition of one or more selected (or all) codons is substituted withmixed-base and/or deoxyinosine residues (Batzer et al., Nucleic AcidRes., 19:5081 (1991); Ohtsuka et al., J. Biol. Chem., 260:2605-2608(1985); Rossolini et al., Mol. Cell. Probes, 8:91-98 (1994)). The termnucleic acid is used interchangeably with gene, cDNA, mRNA,oligonucleotide, and polynucleotide.

A particular nucleic acid sequence also implicitly encompasses “splicevariants” and nucleic acid sequences encoding truncated forms ofproteins. Similarly, a particular protein encoded by a nucleic acidimplicitly encompasses any protein encoded by a splice variant ortruncated form of that nucleic acid. “Splice variants,” as the namesuggests, are products of alternative splicing of a gene. Aftertranscription, an initial nucleic acid transcript may be spliced suchthat different (alternate) nucleic acid splice products encode differentpolypeptides. Mechanisms for the production of splice variants vary, butinclude alternate splicing of exons. Alternate polypeptides derived fromthe same nucleic acid by read-through transcription are also encompassedby this definition. Any products of a splicing reaction, includingrecombinant forms of the splice products, are included in thisdefinition. Nucleic acids can be truncated at the 5′ end or at the 3′end. Polypeptides can be truncated at the N-terminal end or theC-terminal end. Truncated versions of nucleic acid or polypeptidesequences can be naturally occurring or recombinantly created.

The terms “polypeptide,” “peptide” and “protein” are usedinterchangeably herein to refer to a polymer of amino acid residues. Theterms apply to amino acid polymers in which one or more amino acidresidue is an artificial chemical mimetic of a corresponding naturallyoccurring amino acid, as well as to naturally occurring amino acidpolymers and non-naturally occurring amino acid polymer.

The term “amino acid” refers to naturally occurring and synthetic aminoacids, as well as amino acid analogs and amino acid mimetics thatfunction in a manner similar to the naturally occurring amino acids.Naturally occurring amino acids are those encoded by the genetic code,as well as those amino acids that are later modified, e.g.,hydroxyproline, ÿ-carboxyglutamate, and O-phosphoserine Amino acidanalogs refers to compounds that have the same basic chemical structureas a naturally occurring amino acid, i.e., any carbon that is bound to ahydrogen, a carboxyl group, an amino group, and an R group, e.g.,homoserine, norleucine, methionine sulfoxide, methionine methylsulfonium. Such analogs have modified R groups (e.g., norleucine) ormodified peptide backbones, but retain the same basic chemical structureas a naturally occurring amino acid Amino acid mimetics refers tochemical compounds that have a structure that is different from thegeneral chemical structure of an amino acid, but that functions in amanner similar to a naturally occurring amino acid.

Amino acids may be referred to herein by either their commonly knownthree letter symbols or by the one-letter symbols recommended by theIUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise,may be referred to by their commonly accepted single-letter codes.

“Conservatively modified variants” applies to both amino acid andnucleic acid sequences. With respect to particular nucleic acidsequences, conservatively modified variants refers to those nucleicacids which encode identical or essentially identical amino acidsequences, or where the nucleic acid does not encode an amino acidsequence, to essentially identical sequences. Because of the degeneracyof the genetic code, a large number of functionally identical nucleicacids encode any given protein. For instance, the codons GCA, GCC, GCGand GCU all encode the amino acid alanine. Thus, at every position wherean alanine is specified by a codon, the codon can be altered to any ofthe corresponding codons described without altering the encodedpolypeptide. Such nucleic acid variations are “silent variations,” whichare one species of conservatively modified variations. Every nucleicacid sequence herein which encodes a polypeptide also describes everypossible silent variation of the nucleic acid. One of skill willrecognize that each codon in a nucleic acid (except AUG, which isordinarily the only codon for methionine, and TGG, which is ordinarilythe only codon for tryptophan) can be modified to yield a functionallyidentical molecule. Accordingly, each silent variation of a nucleic acidwhich encodes a polypeptide is implicit in each described sequence withrespect to the expression product, but not with respect to actual probesequences.

As to amino acid sequences, one of skill will recognize that individualsubstitutions, deletions or additions to a nucleic acid, peptide,polypeptide, or protein sequence which alters, adds or deletes a singleamino acid or a small percentage of amino acids in the encoded sequenceis a “conservatively modified variant” where the alteration results inthe substitution of an amino acid with a chemically similar amino acid.Conservative substitution tables providing functionally similar aminoacids are well known in the art. Such conservatively modified variantsare in addition to and do not exclude polymorphic variants, interspecieshomologs, and alleles of the invention.

The following eight groups each contain amino acids that areconservative substitutions for one another: 1) Alanine (A), Glycine (G);2) Aspartic acid (D), Glutamic acid (E); 3) Asparagine (N), Glutamine(Q); 4) Arginine (R), Lysine (K); 5) Isoleucine (I), Leucine (L),Methionine (M), Valine (V); 6) Phenylalanine (F), Tyrosine (Y),Tryptophan (W); 7) Serine (S), Threonine (T); and 8) Cysteine (C),Methionine (M) (see, e.g., Creighton, Proteins (1984)).

A “label” or a “detectable moiety” is a composition detectable byspectroscopic, photochemical, biochemical, immunochemical, chemical, orother physical means. For example, useful labels include ³²P,fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonlyused in an ELISA), biotin, digoxigenin, or haptens and proteins whichcan be made detectable, e.g., by incorporating a radiolabel into thepeptide or used to detect antibodies specifically reactive with thepeptide.

The term “recombinant” when used with reference, e.g., to a cell, ornucleic acid, protein, or vector, indicates that the cell, nucleic acid,protein or vector, has been modified by the introduction of aheterologous nucleic acid or protein or the alteration of a nativenucleic acid or protein, or that the cell is derived from a cell somodified. Thus, for example, recombinant cells express genes that arenot found within the native (non-recombinant) form of the cell orexpress native genes that are otherwise abnormally expressed, underexpressed or not expressed at all.

The phrase “specifically (or selectively) binds” or “specifically (orselectively) detects” refers to a binding reaction that is determinativeof the presence of a marker, such as a protein, nucleic acid, ormicrobe, which is often in a heterogeneous population of proteins,nucleic acids, or microbes and other biologics. For example, thepresence of a protein is specifically detected if, under designatedimmunoassay conditions, the specified antibodies bind to a particularprotein at least two times the background and more typically more than10 to 100 times background. Specific binding to an antibody under suchconditions requires an antibody that is selected for its specificity fora particular protein. For example, polyclonal antibodies can be selectedto obtain only those polyclonal antibodies that are specificallyimmunoreactive with the selected antigen and not with other proteins.This selection may be achieved by subtracting out antibodies thatcross-react with other molecules. A variety of immunoassay formats maybe used to select antibodies specifically immunoreactive with aparticular protein. For example, solid-phase ELISA immunoassays areroutinely used to select antibodies specifically immunoreactive with aprotein (see, e.g., Harlow & Lane, Antibodies, A Laboratory Manual(1988) for a description of immunoassay formats and conditions that canbe used to determine specific immunoreactivity). Luminex® xMAPtechnology is particularly well suited for the present invention.Similarly, the presence of a nucleic acid (or of a microbe, the presenceof which can be determined by analyzing the nucleic acid content of themicrobe) is specifically detected if, under designated hybridizationconditions, the specified oligonucleotides bind to a particular nucleicacid target sequence at least two times the background and moretypically more than 10 to 100 times background. Specific binding to anoligonucleotide under such conditions requires an oligonucleotide thatis selected for its specificity for a particular nucleic acid sequence.For example, oligonucleotides can be selected which bind to the targetnucleic acid sequence under stringent hybridization conditions.

The phrase “stringent hybridization conditions” refers to conditionsunder which a probe will hybridize to its target subsequence, typicallyin a complex mixture of nucleic acids, but to no other sequences.Stringent conditions are sequence-dependent and will be different indifferent circumstances. Longer sequences hybridize specifically athigher temperatures. An extensive guide to the hybridization of nucleicacids is found in Tijssen, Techniques in Biochemistry and MolecularBiology—Hybridization with Nucleic Probes, “Overview of principles ofhybridization and the strategy of nucleic acid assays” (1993).Generally, stringent conditions are selected to be about 5-10° C. lowerthan the thermal melting point (T_(m)) for the specific sequence at adefined ionic strength pH. The T_(m) is the temperature (under definedionic strength, pH, and nucleic concentration) at which 50% of theprobes complementary to the target hybridize to the target sequence atequilibrium (as the target sequences are present in excess, at T_(m),50% of the probes are occupied at equilibrium). Stringent conditions mayalso be achieved with the addition of destabilizing agents such asformamide. For selective or specific hybridization, a positive signal isat least two times background, preferably 10 times backgroundhybridization. Exemplary stringent hybridization conditions can be asfollowing: 50% formamide, 5×SSC, and 1% SDS, incubating at 42° C., or,5×SSC, 1% SDS, incubating at 65° C., with wash in 0.2×SSC, and 0.1% SDSat 65° C.

Nucleic acids that do not hybridize to each other under stringentconditions are still substantially identical if the polypeptides whichthey encode are substantially identical. This occurs, for example, whena copy of a nucleic acid is created using the maximum codon degeneracypermitted by the genetic code. In such cases, the nucleic acidstypically hybridize under moderately stringent hybridization conditions.Exemplary “moderately stringent hybridization conditions” include ahybridization in a buffer of 40% formamide, 1 M NaCl, 1% SDS at 37° C.,and a wash in 1×SSC at 45° C. A positive hybridization is at least twicebackground. Those of ordinary skill will readily recognize thatalternative hybridization and wash conditions can be utilized to provideconditions of similar stringency. Additional guidelines for determininghybridization parameters are provided in numerous reference, e.g.,Current Protocols in Molecular Biology, ed. Ausubel, et al., supra.

For PCR, a temperature of about 36° C. is typical for low stringencyamplification, although annealing temperatures may vary between about32° C. and 48° C. depending on primer length. For high stringency PCRamplification, a temperature of about 62° C. is typical, although highstringency annealing temperatures can range from about 50° C. to about65° C., depending on the primer length and specificity. Typical cycleconditions for both high and low stringency amplifications include adenaturation phase of 90° C.-95° C. for 30 sec.-2 min., an annealingphase lasting 30 sec.-2 min., and an extension phase of about 72° C. for1-2 min. Protocols and guidelines for low and high stringencyamplification reactions are provided, e.g., in Innis et al., PCRProtocols, A Guide to Methods and Applications (Academic Press, Inc.,N.Y., 1990).

“Antibody” refers to a polypeptide comprising a framework region from animmunoglobulin gene or fragments thereof that specifically binds andrecognizes an antigen. The recognized immunoglobulin genes include thekappa, lambda, alpha, gamma, delta, epsilon, and mu constant regiongenes, as well as the myriad immunoglobulin variable region genes. Lightchains are classified as either kappa or lambda. Heavy chains areclassified as gamma, mu, alpha, delta, or epsilon, which in turn definethe immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively.Typically, the antigen-binding region of an antibody will be mostcritical in specificity and affinity of binding. Antibodies can bepolyclonal or monoclonal, derived from serum, a hybridoma orrecombinantly cloned, and can also be chimeric, primatized, orhumanized.

An exemplary immunoglobulin (antibody) structural unit comprises atetramer. Each tetramer is composed of two identical pairs ofpolypeptide chains, each pair having one “light” (about 25 kD) and one“heavy” chain (about 50-70 kD). The N-terminus of each chain defines avariable region of about 100 to 110 or more amino acids primarilyresponsible for antigen recognition. The terms variable light chain(V_(L)) and variable heavy chain (V_(H)) refer to these light and heavychains respectively.

Antibodies exist, e.g., as intact immunoglobulins or as a number ofwell-characterized fragments produced by digestion with variouspeptidases. Thus, for example, pepsin digests an antibody below thedisulfide linkages in the hinge region to produce F(ab)′₂ a dimer of Fabwhich itself is a light chain joined to V_(H)-C_(H)1 by a disulfidebond. The F(ab)′₂ may be reduced under mild conditions to break thedisulfide linkage in the hinge region, thereby converting the F(ab)′₂dimer into an Fab′ monomer. The Fab′ monomer is essentially Fab withpart of the hinge region (see Fundamental Immunology (Paul ed., 3d ed.1993). While various antibody fragments are defined in terms of thedigestion of an intact antibody, one of skill will appreciate that suchfragments may be synthesized de novo either chemically or by usingrecombinant DNA methodology. Thus, the term antibody, as used herein,also includes antibody fragments either produced by the modification ofwhole antibodies, or those synthesized de novo using recombinant DNAmethodologies (e.g., single chain Fv) or those identified using phagedisplay libraries (see, e.g., McCafferty et al., Nature, 348:552-554(1990)).

In one embodiment, the antibody is conjugated to an “effector” moiety.The effector moiety can be any number of molecules, including labelingmoieties such as radioactive labels or fluorescent labels, or can be atherapeutic moiety. In one aspect the antibody modulates the activity ofthe protein.

III. Diagnostic and Prognostic Methods

The present invention provides methods of diagnosing a pancreatic cancerby examining protein or RNA expression of transcriptome or microbebiomarkers listed in FIGS. 4 and 5 and in Table 4, or a combinationthereof, including wild-type, truncated or alternatively spliced forms,in biological samples. Diagnosis involves determining the level of apolynucleotide or polypeptide of the invention in a patient and thencomparing the level to a baseline or range. Typically, the baselinevalue is representative of a polynucleotide or polypeptide of theinvention in a healthy person not suffering from cancer, as measuredusing biological sample such as a tissue sample (e.g., tongue or lymphtissue), serum, blood, or saliva. Variation of levels of apolynucleotide or polypeptide of the invention from the baseline range(either up or down) indicates that the patient has a pancreatic cancer.

PCR assays such as Taqman® allelic discrimination assay available fromApplied Biosystems can be used to identify RNA. In another embodiment,mass spectroscopy can be used to detect either nucleic acid or protein.Any antibody-based technique for determining a level of expression of aprotein of interest can be used. For example, immunoassays such asELISA, Western blotting, flow cytometry, immunofluorescence, andimmunohistochemistry can be used to detect protein in patient samples.Combinations of the above methods, such as those employed in theLuminex® xMAP technology can also be used in the present invention.

Analysis of a protein or nucleic acid can be achieved, for example, byhigh pressure liquid chromatography (HPLC), alone or in combination withmass spectrometry (e.g., MALDI/MS, MALDI-TOF/MS, tandem MS, etc.).

Analysis of nucleic acid can be achieved using routine techniques suchas northern analysis, reverse-transcriptase polymerase chain reaction(RT-PCR), microarrays, sequence analysis, or any other methods based onhybridization to a nucleic acid sequence that is complementary to aportion of the marker coding sequence (e.g., slot blot hybridization)are also within the scope of the present invention. Applicable PCRamplification techniques are described in, e.g., Ausubel et al.,Theophilus et al., and Innis et al., supra. General nucleic acidhybridization methods are described in Anderson, “Nucleic AcidHybridization,” BIOS Scientific Publishers, 1999. Amplification orhybridization of a plurality of nucleic acid sequences (e.g., genomicDNA, mRNA or cDNA) can also be performed from mRNA or cDNA sequencesarranged in a microarray. Microarray methods are generally described inHardiman, “Microarrays Methods and Applications: Nuts & Bolts,” DNAPress, 2003; and Baldi et al., “DNA Microarrays and Gene Expression:From Experiments to Data Analysis and Modeling,” Cambridge UniversityPress, 2002.

Non-limiting examples of sequence analysis include Sanger sequencing,capillary array sequencing, thermal cycle sequencing (Sears et al.,Biotechniques, 13:626-633 (1992)), solid-phase sequencing (Zimmerman etal., Methods Mol. Cell Biol., 3:39-42 (1992)), sequencing with massspectrometry such as matrix-assisted laser desorption/ionizationtime-of-flight mass spectrometry (MALDI-TOF/MS; Fu et al., NatureBiotech., 16:381-384 (1998)), and sequencing by hybridization (Chee etal., Science, 274:610-614 (1996); Drmanac et al., Science, 260:1649-1652(1993); Drmanac et al., Nature Biotech., 16:54-58 (1998)). Non-limitingexamples of electrophoretic analysis include slab gel electrophoresissuch as agarose or polyacrylamide gel electrophoresis, capillaryelectrophoresis, and denaturing gradient gel electrophoresis

A detectable moiety can be used in the assays described herein. A widevariety of detectable moieties can be used, with the choice of labeldepending on the sensitivity required, ease of conjugation with theantibody, stability requirements, and available instrumentation anddisposal provisions. Suitable detectable moieties include, but are notlimited to, radionuclides, fluorescent dyes (e.g., fluorescein,fluorescein isothiocyanate (FITC), Oregon Green™, rhodamine, Texas red,tetrarhodimine isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescentmarkers (e.g., green fluorescent protein (GFP), phycoerythrin, etc.),autoquenched fluorescent compounds that are activated bytumor-associated proteases, enzymes (e.g., luciferase, horseradishperoxidase, alkaline phosphatase, etc.), nanoparticles, biotin,digoxigenin, and the like.

Immunoassay techniques and protocols are generally described in Priceand Newman, “Principles and Practice of Immunoassay,” 2nd Edition,Grove's Dictionaries, 1997; and Gosling, “Immunoassays: A PracticalApproach,” Oxford University Press, 2000. A variety of immunoassaytechniques, including competitive and non-competitive immunoassays, canbe used (see, e.g., Self et al., Curr. Opin. Biotechnol., 7:60-65(1996)). The term immunoassay encompasses techniques including, withoutlimitation, enzyme immunoassays (EIA) such as enzyme multipliedimmunoassay technique (EMIT), enzyme-linked immunosorbent assay (ELISA),IgM antibody capture ELISA (MAC ELISA), and microparticle enzymeimmunoassay (MEIA); capillary electrophoresis immunoassays (CEIA);radioimmunoassays (RIA); immunoradiometric assays (IRMA); fluorescencepolarization immunoassays (FPIA); and chemiluminescence assays (CL). Ifdesired, such immunoassays can be automated. Immunoassays can also beused in conjunction with laser induced fluorescence (see, e.g.,Schmalzing et al., Electrophoresis, 18:2184-93 (1997); Bao, J.Chromatogr. B. Biomed. Sci., 699:463-80 (1997)). Liposome immunoassays,such as flow-injection liposome immunoassays and liposome immunosensors,are also suitable for use in the present invention (see, e.g., Rongen etal., J. Immunol. Methods, 204:105-133 (1997)). In addition, nephelometryassays, in which the formation of protein/antibody complexes results inincreased light scatter that is converted to a peak rate signal as afunction of the marker concentration, are suitable for use in themethods of the present invention. Nephelometry assays are commerciallyavailable from Beckman Coulter (Brea, Calif.; Kit #449430) and can beperformed using a Behring Nephelometer Analyzer (Fink et al., J. Clin.Chem. Clin. Biochem., 27:261-276 (1989)).

Specific immunological binding of the antibody to a protein can bedetected directly or indirectly. Direct labels include fluorescent orluminescent tags, metals, dyes, radionuclides, and the like, attached tothe antibody. An antibody labeled with iodine-125 (¹²⁵I) can be used. Achemiluminescence assay using a chemiluminescent antibody specific forthe protein marker is suitable for sensitive, non-radioactive detectionof protein levels. An antibody labeled with fluorochrome is alsosuitable. Examples of fluorochromes include, without limitation, DAPI,fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin,R-phycoerythrin, rhodamine, Texas red, and lissamine, Indirect labelsinclude various enzymes well known in the art, such as horseradishperoxidase (HRP), alkaline phosphatase (AP), ÿ-galactosidase, urease,and the like. A horseradish-peroxidase detection system can be used, forexample, with the chromogenic substrate tetramethylbenzidine (TMB),which yields a soluble product in the presence of hydrogen peroxide thatis detectable at 450 nm. An alkaline phosphatase detection system can beused with the chromogenic substrate p-nitrophenyl phosphate, forexample, which yields a soluble product readily detectable at 405 nm.Similarly, a ÿ-galactosidase detection system can be used with thechromogenic substrate o-nitrophenyl-ÿ-D-galactopyranoside (ONPG), whichyields a soluble product detectable at 410 nm. An urease detectionsystem can be used with a substrate such as urea-bromocresol purple(Sigma Immunochemicals; St. Louis, Mo.).

A signal from the direct or indirect label can be analyzed, for example,using a spectrophotometer to detect color from a chromogenic substrate;a radiation counter to detect radiation such as a gamma counter fordetection of ¹²⁵I; or a fluorometer to detect fluorescence in thepresence of light of a certain wavelength. For detection ofenzyme-linked antibodies, a quantitative analysis can be made using aspectrophotometer such as an EMAX Microplate Reader (Molecular Devices;Menlo Park, Calif.) in accordance with the manufacturer's instructions.If desired, the assays of the present invention can be automated orperformed robotically, and the signal from multiple samples can bedetected simultaneously.

The antibodies can be immobilized onto a variety of solid supports, suchas magnetic or chromatographic matrix particles, the surface of an assayplate (e.g., microtiter wells), pieces of a solid substrate material ormembrane (e.g., plastic, nylon, paper), and the like. An assay strip canbe prepared by coating the antibody or a plurality of antibodies in anarray on a solid support. This strip can then be dipped into the testsample and processed quickly through washes and detection steps togenerate a measurable signal, such as a colored spot.

Useful physical formats comprise surfaces having a plurality ofdiscrete, addressable locations for the detection of a plurality ofdifferent biomarkers. Such formats include protein microarrays, or“protein chips” (see, e.g., Ng et al., J. Cell Mol. Med., 6:329-340(2002)) and certain capillary devices (see, e.g., U.S. Pat. No.6,019,944). In these embodiments, each discrete surface location maycomprise antibodies to immobilize one or more protein markers fordetection at each location. Surfaces may alternatively comprise one ormore discrete particles (e.g., microparticles or nanoparticles)immobilized at discrete locations of a surface, where the microparticlescomprise antibodies to immobilize one or more protein markers fordetection.

The analysis can be carried out in a variety of physical formats. Forexample, the use of microtiter plates or automation could be used tofacilitate the processing of large numbers of test samples.Alternatively, single sample formats could be developed to facilitatediagnosis or prognosis in a timely fashion.

IV. Compositions, Kits, and Arrays

The invention provides compositions, kits and integrated systems forpracticing the assays described herein using polynucleotides andpolypeptides of the invention, antibodies specific for polypeptides orpolynucleotides of the invention, etc.

The invention provides assay compositions for use in solid phase assays;such compositions can include, for example, one or more polynucleotidesor polypeptides of the invention immobilized on a solid support, and alabeling reagent. In each case, the assay compositions can also includeadditional reagents that are desirable for hybridization. Modulators ofexpression or activity of polynucleotides or polypeptides of theinvention can also be included in the assay compositions.

The invention also provides kits for carrying out the diagnostic assaysof the invention. The kits typically include a probe that comprises anantibody or nucleic acid sequence that specifically binds topolypeptides or polynucleotides of the invention, and a label fordetecting the presence of the probe. The kits may include severalpolynucleotide sequences encoding polypeptides of the invention.

Optical images viewed (and, optionally, recorded) by a camera or otherrecording device (e.g., a photodiode and data storage device) areoptionally further processed in any of the embodiments herein, e.g., bydigitizing the image and storing and analyzing the image on a computer.A variety of commercially available peripheral equipment and software isavailable for digitizing, storing and analyzing a digitized video ordigitized optical images.

One conventional system carries light from the specimen field to acooled charge-coupled device (CCD) camera, in common use in the art. ACCD camera includes an array of picture elements (pixels). The lightfrom the specimen is imaged on the CCD. Particular pixels correspondingto regions of the specimen are sampled to obtain light intensityreadings for each position. Multiple pixels are processed in parallel toincrease speed. The apparatus and methods of the invention are easilyused for viewing any sample, e.g., by fluorescent or dark fieldmicroscopic techniques.

V. Examples

The following examples are offered to illustrate, but not to limit, theclaimed invention.

Here, we report the use of two high throughput discovery approaches toidentify discriminatory biomarkers in saliva for the non-invasivedetection of early stage pancreatic cancer. Our results demonstrate thatthe profiles of salivary transcriptome and microflora are significantlydifferent between patients with early stage pancreatic cancer andhealthy controls. The salivary biomarkers identified and validated inthese platforms possess great discriminatory power for the detection ofearly stage pancreatic cancer, with high specificity and sensitivity.

Patients and Methods

Patients. This study, which was approved by the UCLA InstitutionalReview Board, started sample collection in February 2006. It had adiscovery phase, followed by an independent validation phase. Allsubjects with clinically diagnosed pancreatic cancer, chronicpancreatitis and healthy control were recruited from the UCLA MedicalCenter. The saliva bank of pancreatic disease at the UCLA DentalResearch Institute has collected 283 saliva samples since 2006. Ofthese, 114 samples, from 42 pancreatic cancer patients, 30 chronicpancreatitis patients and 42 healthy control individuals (Table 1), wereselected for the discovery and validation phase of this study. Inclusioncriteria of disease patients consisted of confirmed diagnosis of primarydisease (stage I pancreatic cancer and chronic pancreatitis). Exclusioncriteria included chemotherapy or radiation therapy prior to salivacollection and a diagnosis of other malignancies within 5 y from thetime of saliva collection. Written informed consents and questionnairedata sheets were obtained from all patients who agreed to serve assaliva donors. The information on individual characteristics, such asage, gender, ethnicity, smoking history and drinking history, ispresented in FIG. 3. Healthy control individuals were matched for age,gender and ethnicity to the cancer group. Unstimulated saliva sampleswere collected and processed as previously described^(12,23). Bothsupernatant and pellet samples were reserved at −80° C. prior to assay.

Study design. Of the 114 samples, 12 pancreatic cancer samples and 12healthy control samples were chosen for the discovery phase. Thetranscriptomic approach profiled the saliva supernatant samples from 12pancreatic cancer patients and 12 healthy control subjects using theAffymetrix HG U133 Plus 2.0 Array. The microbial approach profiled thesaliva pellet samples from 10 pancreatic cancer patients and 10 healthycontrol subjects using the Human Oral Microbe Identification Microarray(HOMIM) platform²⁴. Biomarkers identified from both platforms were firstverified using the discovery sample set. An independent sample set,including 30 pancreatic cancer patients, 30 chronic pancreatitispatients and 30 healthy control subjects, was used for the biomarkervalidation phase (FIG. 1).

Salivary transcriptomic profiling. RNA was isolated from 330 μL ofsaliva supernatant using the MagMax™ Viral RNA Isolation Kit (Ambion,Austin, Tex.). This process was automated using KingFisher mL technology(Thermo Fisher Scientific), followed by TURBO™ DNase treatment (Ambion).Extracted RNA was linearly amplified using the RiboAmp RNA Amplificationkit (Molecular Devices, Sunnyvale, Calif.). After purification, cDNA wasin vitro transcribed and biotinylated using GeneChip Expression3′-Amplification Reagents for in vitro transcription labeling(Affymetrix, Santa Clara, Calif.). Chip hybridization and scanning wereperformed at the UCLA microarray core facility.

U133 Plus 2.0 Array data analysis. The analysis was performed using R2.7.0 (http://www.r-project.org). The Probe Logarithmic Intensity ErrorEstimation (PLIER) expression measures were computed after backgroundcorrection and quantile normalization for each microarray dataset.Probeset-level quantile normalization was performed across all samplesto make the effect sizes similar among all datasets. Finally, for everyprobeset, the two-sample t-test was applied to identify differentialexpression between cancer and healthy control. After obtaining theestimates and the p-values of each probeset, we corrected the p-valuesfor false discovery rate (FDR).

Validation of mRNA biomarkers using quantitative PCR (qPCR). Theselected mRNA biomarkers were first verified by qPCR using the discoverysample set (12 pancreatic cancer and 12 healthy control) as describedpreviously^(16,25). qPCR primers were designed using Primer Express 3.0software (Applied Biosystems, FosterCity, Calif.) (Table 2). All primerswere synthesized by Sigma-Genosys (Woodlands, Tex.). The amplicons wereintron spanning whenever possible. qPCR was carried out in duplicate.Verified biomarkers were then assayed by qPCR in the set of 90independent samples. The Wilcoxon test was used to compare thebiomarkers between groups.

Salivary microflora profiling and microbial biomarker validation. Tenpancreatic cancer pellets and 10 matched control pellets were used forthe microbial profiling. Bacterial DNA was extracted using UltraCleanMicrobial DNA Isolation Kit (MO BIO Laboratories, Carlsbad, Calif.). PCRamplification using 16S universal primers²⁶ was performed at the ForsythInstitute, followed by hybridization to the Human Oral MicrobeIdentification Microarray (HOMIM)²⁴. Selection of bacterial candidateswas based on p-value by Wilcoxon rank-sum test (P<0.05). Quantities ofbacterial species in the original DNA samples were determined by qPCR.Specific primers (Table 3) were designed for the 16S rRNA genes of thebacterial biomarker candidates. qPCR was carried out in duplicate inreaction volumes of 10 μL using power SYBR-Green Master Mix (AppliedBiosystems, Foster City, Calif.) for 15 min at 95° C. for initialdenaturing, followed by 40 cycles of 95° C. for 30 sec and 60° C. for 30sec in the ABI 7900HT Fast Real Time PCR system (Applied Biosystems).Verified microbial biomarkers were then subjected to independentclinical validation by qPCR. The Wilcoxon test was used to compare thebiomarkers between groups.

Predictive model building and evaluation. The logistic regression (LR)method was used in prediction model building. For each validatedbiomarker, we constructed the receiver operating characteristic (ROC)curve and computed the Area Under Curve (AUC) value by numericalintegration of the ROC curve. Next, the validated salivary biomarkerswere fit into logistic regression models (separately for mRNA andmicrobial biomarkers, and separately for each group comparisons) andstepwise backward model selection was performed to determine finalcombinations of biomarkers. For each of these models, the predictedprobability for each subject was obtained and was used to construct ROCcurves. The standard error of the AUC and the 95% confidence interval(CI) for the ROC curve was computed according to previouspublications^(27,28). The sensitivity and specificity for the biomarkercombinations were estimated by identifying the cutoff-point of thepredicted probability that yielded the highest sum of sensitivity andspecificity.

Identification and Validation of mRNA Biomarkers for Pancreatic Cancer

Transcriptomic profiling revealed that 958 genes exhibited >2 foldup-regulation and 691 genes exhibited >2 fold down-regulation in thesaliva of pancreatic cancer patients, relative to the healthy controls(n=24, P<0.05). These transcripts identified were unlikely to beattributed to chance (χ² test, P<0.0001), considering the false positivewith P<0.05. Using a predefined criterion of a change inregulation >4-fold, and a more stringent cutoff of p-value <0.01, weidentified 49 up-regulated and 21 down-regulated transcripts inpancreatic cancer samples.

Quantitative PCR was performed to verify the microarray results on thediscovery sample set. All 49 up-regulated and 21 down-regulatedtranscripts were evaluated. The results confirmed that the relative RNAexpression levels of 23 up-regulated and 12 down-regulated transcripts,as measured by qPCR, were consistent with the results of the microarrayanalysis. The biological functions of these genes and their products arepresented in Table 4. These verified candidates were then subjected toan independent validation by qPCR. As shown in FIG. 4, a total of 7up-regulated and 5 down-regulated genes were validated based on the qPCRdata of 30 pancreatic cancer patients and 30 healthy control subjects.All 12 mRNA biomarkers showed significant difference between pancreaticcancer and healthy controls (P<0.05, n=60), yielding ROC-plot AUC valuesbetween 0.682 and 0.823. The expression patterns of these mRNAbiomarkers were consistent with those retrieved by microarray assay(up/down-regulation and fold change). Importantly, the expression levelsof six up-regulated mRNAs (MBD3L2, KRAS, STIM2, ACRV1, DMD, CABLES1) andthree down-regulated mRNAs (TK2, GLTSCR2, CDKL3) were also significantlydifferent between pancreatic cancer and chronic pancreatitis (P<0.05,n=60). The expression level of all 12 up/down-regulated mRNAs weresignificantly different between pancreatic cancer (n=30) and non-cancersubjects (chronic pancreatitis and healthy control, n=60) (P<0.05),yielding ROC-plot AUC values between 0.661 and 0.791 (FIG. 4).

Identification and Validation of Bacterial Biomarkers for PancreaticCancer

Based on the microarray data of 410 oligonucleotide probes on the HOMIM,16 species/clusters showing significant difference between pancreaticcancer and healthy controls (P<0.05, n=20) were selected as biomarkercandidates for qPCR verification of the microarray results. These 16species/clusters represented 6 different genus, including Streptococcus(3 species/groups), Prevotella (4 species/groups), Campylobacter (4species/groups), Granulicatella (2 species), Atopobium (1 species), andNeisseria (2 species). Using the discovery sample set, 6 out of 16species were confirmed by qPCR (FIG. 5). These candidates were thensubjected to the independent validation by qPCR (28 pancreatic cancer,27 chronic pancreatitis and 28 healthy control. Two pancreatic cancer, 3chronic pancreatitis and 3 healthy control samples did not have usableDNA). Two bacterial markers (Neisseria elongata and Streptococcus mitis)showed significant difference between pancreatic cancer and healthycontrols (P<0.05, n=56), yielding ROC-plot AUC values of 0.657 and0.680, respectively. The levels of both bacterial markers were decreasedin pancreatic cancer as shown by qPCR, which were consistent with theresults obtained by HOMIM. The levels of an increased species(Granulicatella adiacens) and a decreased species (Streptococcus mitis)were significantly different between pancreatic cancer and chronicpancreatitis (P<0.05, n=55). The levels of G. adiacens and S. mitis werealso significantly different between pancreatic cancer (n=28) andnon-cancer subjects (chronic pancreatitis and healthy controls, n=55)(P<0.05), yielding ROC-plot AUC values of 0.544 and 0.682, respectively(FIG. 5).

Prediction Models Using the Validated mRNA and Bacterial Biomarkers

To demonstrate the clinical utility of salivary mRNAs and bacteriabiomarkers for pancreatic cancer discrimination, logistic regressionmodels were built based on different combinations of biomarkers forthree levels of clinical discrimination: pancreatic cancer vs. healthycontrol; pancreatic cancer vs. chronic pancreatitis and pancreaticcancer vs. non-cancer (healthy control+chronic pancreatitis) (FIG. 6).For pancreatic cancer vs. healthy control, the logistic regression modelwith the combination of four mRNA biomarkers (KRAS, MBD3L2, ACRV1 andCDKL3) yielded a ROC-plot AUC value of 0.973 (95% CI, 0.895 to 0.997;P<0.0001) with 93.3% sensitivity and 100% specificity in distinguishingpancreatic cancer patients from healthy control subjects. The logisticregression model with the combination of two bacterial biomarkers (N.elongata and S. mitis) yielded a ROC-plot AUC value of 0.895 (95% CI,0.784 to 0.961; P<0.0001) with 96.4% sensitivity and 82.1% specificityin distinguishing pancreatic cancer patients from healthy subjects. Forpancreatic cancer vs. chronic pancreatitis, the logistic regressionmodel with the combination of three mRNA biomarkers (CDKL3, MBD3L2,KRAS) yielded a ROC-plot AUC value of 0.981 (95% CI, 0.907 to 0.997;P<0.0001) with 96.7% sensitivity and 96.7% specificity in distinguishingpancreatic cancer patients from chronic pancreatitis. Most importantly,for the discrimination of pancreatic cancer vs. non-cancer, the logisticregression model with the combination of four mRNA biomarkers (KRAS,MBD3L2, ACRV1 and DPM1) could differentiate pancreatic cancer patientsfrom all non-cancer subjects, yielding a ROC-plot AUC value of 0.971(95% CI, 0.911 to 0.994; P<0.0001). The four-biomarker logisticregression model provided the highest discriminatory power fordifferentiating pancreatic cancer from non-cancer subjects. Using acutoff of 0.433, a sensitivity of 90.0% and a specificity of 95.0% wasobtained for this four-biomarker logistic regression model (FIG. 2).

The effects of age and smoking history on the validated biomarkers wereevaluated using linear regression models since these two factors weresignificantly different among the groups used for validation (Table 6).The regression models were used to determine if age and smoking hadindependent effects on the biomarkers which may have biased thediagnostic models. To avoid ecological correlation we performed theseanalyses separately within each group. We found that neither age norsmoking had effects on the markers more than we would expect by chance(only 2 out of 90 [2 covariates×15 markers×3 groups] tests weresignificant at α=0.05).

Discussion

The harnessing of valuable disease-specific biomarkers from body fluidsamples such as saliva is imperative in current biomedical research. Thesalivary biomarkers that were identified here are highly discriminatoryfor the detection of early pancreatic cancer, with high sensitivity andspecificity. Additionally, the methods presented here, a saliva-baseddiagnostic and early detection test for pancreatic cancer, are simpleand non-invasive.

Understanding the profiles of molecular shifts in any particular canceris extremely useful because it will become possible to correlate thecancer with its molecular signatures. Consistent with previous studies,our high-throughput analysis indicates that the mRNA in salivasupernatant is relatively stable and informative, and is a suitablesource of biomarkers^(16,17,29-32). The consistency between differentmRNA analysis methods (microarray and qPCR) demonstrates that thealteration of the salivary mRNA signatures between cancer group andcontrol group can serve as biomarkers for early detection of pancreaticcancer. Out of the 12 validated mRNA biomarkers, several genes, e.g.MBD3L2, GLTSCR2 and TPT1, have been 33-41 linked to carcinogenesis³³⁻⁴¹.Of particular interest is that KRAS, a frequently mutated moleculartarget in pancreatic cancer^(42,43), is a discriminatory biomarker insaliva. It remains to be investigated whether the aberrant expressionsof these genes are mediated by salivary glands or by other mechanisms.It has been shown that there is a disease-specific profile change insalivary mRNA biomarkers using the rodent models for systemic diseasedevelopment⁴⁴.

The HOMIM profiling of microflora in the saliva pellet revealed thatmicrobial composition shifts significantly between early pancreaticcancer and control subjects, providing informative signatures forbiomarker discovery. A recent prospective study provided a pioneeringlink between oral health and the risk of pancreatic cancer⁴⁵. However,it is unclear whether the variation in bacterial abundance is aderivational reflection of cancer onset due to the change of oralniches.

Bearing in mind that it is unlikely that a single biomarker will detecta specific cancer with high specificity and sensitivity, we usedlogistic regression to evaluate the combinations of biomarkers. Thecombination of multiple biomarkers increased the ROC-plot AUC values tomuch higher levels. It is particularly notable that the validatedbiomarkers can also discriminate early stage pancreatic cancer fromchronic pancreatitis with high sensitivity and specificity,demonstrating that these salivary biomarkers are specific for thedetection of early pancreatic cancer without the complication of chronicpancreatitis.

The determination of specific profiles of molecular changes in aspecific cancer types is important because it is possible that thedifferent cancers may have overlapping signatures. We have evaluated thespecificity of the 12 validated mRNA biomarkers against other microarraydiscovery studies that have been performed in our laboratory on diversecancers, including oral cancer (HG U133A)¹⁷, breast cancer (HG U133 Plus2.0), and lung cancer (HG U133 Plus 2.0) (unpublished data). With theexception of TK2 that showed significant variation in lung cancer, noneof the other 11 mRNAs/transcripts were significantly altered in othercancer microarray studies (Table 5). In addition, all bacterialbiomarkers validated in this study were also compared to another HOMIMprofiling study using lung cancer saliva pellet (unpublished data). Nonewere included in the list of significant altered species in themicroflora profile of lung cancer. All these cross-disease comparisonsclearly demonstrated that the validated mRNA biomarkers and bacterialbiomarkers in saliva are specific for pancreatic cancer.

TABLE 1 Sample information Discovery smok- diag- set number Ethnicityage gender ing drinking nosis Discovery sample set (12 PanCAN, 12healthy control) D-PanCAN-001 caucasian 75 m No No PC D-PanCAN-002 asian76 f No No PC D-PanCAN-003 caucasian 60 m No No PC D-PanCAN-004caucasian 71 f No No PC D-PanCAN-005 caucasian 65 f No No PCD-PanCAN-006 Hispanic 83 f No No PC D-PanCAN-007 hispanic 65 m No Yes PCD-PanCAN-008 caucasian 68 m No No PC D-PanCAN-009 caucasian 51 m No NoPC D-PanCAN-010 caucasian 81 m No No PC D-PanCAN-011 caucasian 67 m NoNo PC D-PanCAN-012 caucasian 71 m No No PC D-Ctrl-001 caucasian 74 m NoNo Normal D-Ctrl-002 caucasian 51 f No No Normal D-Ctrl-003 caucasian 66m No No Normal D-Ctrl-004 caucasian 54 f No No Normal D-Ctrl-005caucasian 73 m No No Normal D-Ctrl-006 caucasian 79 m No No NormalD-Ctrl-007 hispanic 73 f No No Normal D-Ctrl-008 caucasian 82 m No NoNormal D-Ctrl-009 caucasian 65 m No No Normal D-Ctrl-010 caucasian 57 mNo No Normal D-Ctrl-011 hispanic 86 m No No Normal D-Ctrl-012 asian 49 fNo No Normal Validation sample set (30 PanCAN, 30 healthy control, 30Chronic pancreatitis) V-PanCAN-001 african 76 f No No PC americanV-PanCAN-002 hispanic 74 m No No PC V-PanCAN-003 Asian 73 f No No PCV-PanCAN-004 caucasian 67 m Yes No PC V-PanCAN-005 caucasian 57 m No NoPC V-PanCAN-006 caucasian 82 m No No PC V-PanCAN-007 caucasian 75 m NoNo pc V-PanCAN-008 caucasian 66 m No No pc V-PanCAN-009 caucasian 53 mNo No pc V-PanCAN-010 caucasian 54 m Yes No pc V-PanCAN-011 caucasian 90m No No pc V-PanCAN-012 caucasian 82 m No No pc V-PanCAN-013 asian 66 mNo No pc V-PanCAN-014 asian 53 f No No pc V-PanCAN-015 caucasian 72 m NoYes pc V-PanCAN-016 hispanic 69 f No No pc V-PanCAN-017 asian 82 f No Nopc V-PanCAN-018 caucasian 57 f Yes No pc V-PanCAN-019 caucasian 79 m NoNo pc V-PanCAN-020 caucasian 70 f No No pc V-PanCAN-021 caucasian 79 mNo No pc V-PanCAN-022 caucasian 80 f Yes No pc V-PanCAN-023 hispanic 78f No Yes pc V-PanCAN-024 caucasian 66 m Yes No pc V-PanCAN-025 caucasian61 m No No pc V-PanCAN-026 caucasian 82 m No No pc V-PanCAN-027caucasian 56 m No No pc V-PanCAN-028 african 74 f No No pc americanV-PanCAN-029 hispanic 75 m No No pc V-PanCAN-030 caucasian 40 f No No pcV-Ctrl-001 african 73 f No No Normal american V-Ctrl-002 caucasian 59 mYes No Normal V-Ctrl-003 Asian 73 f No No Normal V-Ctrl-004 caucasian 59m No No Normal V-Ctrl-005 caucasian 80 m No Yes Normal V-Ctrl-006caucasian 54 m Yes No Normal V-Ctrl-007 caucasian 76 m No No NormalV-Ctrl-008 caucasian 75 m No No Normal V-Ctrl-009 caucasian 73 f No NoNormal V-Ctrl-010 caucasian 45 f No No Normal V-Ctrl-011 caucasian 68 mNo No Normal V-Ctrl-012 caucasian 70 m No No Normal V-Ctrl-013 caucasian75 m No No Normal V-Ctrl-014 caucasian 57 m No No Normal V-Ctrl-015caucasian 71 m No Yes Normal V-Ctrl-016 caucasian 80 f No No NormalV-Ctrl-017 asian 69 f No No Normal V-Ctrl-018 african 68 f No No Normalamerican V-Ctrl-019 Asian 69 f No No Normal V-Ctrl-020 caucasian 51 m NoNo Normal V-Ctrl-021 caucasian 59 f No No Normal V-Ctrl-022 hispanic 70f No No Normal V-Ctrl-023 hispanic 49 m No No Normal V-Ctrl-024 hispanic75 m No No Normal V-Ctrl-025 caucasian 56 m No No Normal V-Ctrl-026caucasian 59 m No No Normal V-Ctrl-027 caucasian 49 m No No NormalV-Ctrl-028 asian 60 m No Yes Normal V-Ctrl-029 caucasian 63 m No NoNormal V-Ctrl-030 hispanic 63 f No No Normal V-CP-001 caucasian 52 m YesNo CP V-CP-002 caucasian 45 m No No CP V-CP-003 caucasian 67 m Yes No CPV-CP-004 asian 65 m No No CP V-CP-005 caucasian 62 m No No CP V-CP-006caucasian 42 m Yes No CP V-CP-007 caucasian 61 m Yes No CP V-CP-008african 52 f Yes No CP american V-CP-009 hispanic 45 f No No CP V-CP-010hispanic 27 m No Yes CP V-CP-011 caucasian 49 m No No CP V-CP-012african 57 f No No CP american V-CP-013 caucasian 43 m No No CP V-CP-014Asian 63 m Yes No CP V-CP-015 caucasian 51 f Yes No CP V-CP-016caucasian 59 m No Yes CP V-CP-017 caucasian 49 f No No CP V-CP-018caucasian 52 f Yes No CP V-CP-019 caucasian 62 f No No CP V-CP-020hispanic 72 f No No CP V-CP-021 caucasian 48 f No No CP V-CP-022 asian35 m Yes No CP V-CP-023 caucasian 72 f No No CP V-CP-024 hispanic 70 fNo No CP V-CP-025 caucasian 54 m Yes No CP V-CP-026 caucasian 51 m YesNo CP V-CP-027 caucasian 47 f No No CP V-CP-028 asian 64 m No No CPV-CP-029 caucasian 54 f Yes Yes CP V-CP-030 caucasian 59 f No No CP

TABLE 2 Primers of 35 confirmed transcripts and GAPDH Gene PrimerPrimer sequences symbol name (5′-3′) ACRV1 ACRV1-OFGTCTTCGTGGAGAGGGAACCT (SEQ ID NO: 1) ACRV1-IF GGGAACCTGCATCACTCAGAAT(SEQ ID NO: 2) ACRV1-IR AGTTTTCCACCTTCAAAGATCTTCTT (SEQ ID NO: 3)ACRV1-OR CACACCCTTGAACCATGAATTG (SEQ ID NO: 4) CDC14B CDC14B-OFCTGCCCATTGTTTGGTTGC (SEQ ID NO: 5) CDC14B-IF GTTTGGTTGCCAGTCATACAAATTA(SEQ ID NO: 6) CDC14B-IR ATTGCTGTTTCCAAGGGGAA (SEQ ID NO: 7) CDC14B-ORTAAGCCGACATTATTTGGGATTG (SEQ ID NO: 8) ASH2L ASH2L-OFCTGTCTCAAATGTTCTCCCAAAGAT (SEQ ID NO: 9) ASH2L-IFCAAATGTTCTCCCAAAGATGCTAA (SEQ ID NO: 10) ASH2L-IRCAGTCCTACCCAGCCTTTTAACTT (SEQ ID NO: 11) ASH2L-OR GCAGTCTCCCGCAGTCCTAC(SEQ ID NO: 12) STIM2 STIM2-OF GAAAGCCACGATGGACTTACAAG (SEQ ID NO: 13)STIM2-IF TTAATGGACTCGTAAGCCAGCAT (SEQ ID NO: 14) STIM2-IRAGAAGATGCTCTGGTAAACAAGAAATT (SEQ ID NO: 15) STIM2-ORCTCTGTGGAAAGATAAGAAGATGCTCT (SEQ ID NO: 16) GPR124 GPR124-OFTAGAGGATCTCATGACACCATACACA (SEQ ID NO: 17) GPR124-IFCCCATCATTGCCTGTGAATG (SEQ ID NO: 18) GPR124-IR CCCAGCAGTATCAACCCTCAG(SEQ ID NO: 19) GPR124-OR CCCTCTGCTTGTGGAGTGGT (SEQ ID NO: 20) LILRA2LILRA2-OF GACAGATCTGATGATCCCAGGAG (SEQ ID NO: 21) LILRA2-IFGGCTCTGGAGGACAATCTAGGA (SEQ ID NO: 22) LILRA2-IRCTGTCTCTAGAAATGACCAGCATACAG (SEQ ID NO: 23) LILRA2-ORTGATTGCTGTCTCTAGAAATGACCA (SEQ ID NO: 24) ENG ENG-OF GCAAGAACAGTGGGCGTTG(SEQ ID NO: 25) ENG-IF GAGCCTAGCTCCTGCCACAT (SEQ ID NO: 26) ENG-IRAGGACAAGCAGCTTGGCTACTC (SEQ ID NO: 27) ENG-OR CAGGACAAGCAGCTTGGCTAC(SEQ ID NO: 28) RBM24 RBM24-OF GGTTAGCATTTTTATGGACTTTCTCC(SEQ ID NO: 29) RBM24-IF GGACTTTCTCCATTATCACTGGATTT (SEQ ID NO: 30)RBM24-IR TGCACAGGAGAGTCATGTCTACATT (SEQ ID NO: 31) RBM24-ORGAATAAATAATTTGCACAGGAGAGTCAT (SEQ ID NO: 32) LRRK1 LRRK1-OFGGGAAACTCAATCAGCAGGACT (SEQ ID NO: 33) LRRK1-IF CAGGACTTCAGAAAGGGCCTT(SEQ ID NO: 34) LRRK1-IR CTCCAGCTGCGTCCAAATTT (SEQ ID NO: 35) LRRK1-ORAAACAAACAGGGCCTGTGCT (SEQ ID NO: 36) DMXL2 DMXL2-OFGATGTATTTCCTTGGTTATGACCAAA (SEQ ID NO: 37) DMXL2-IFGTTGAGATACTGAAACTAATGTCTGTGTGT (SEQ ID NO: 38) DMXL2-IRTTAACATGATAAGACAATTTGCTGGTAA (SEQ ID NO: 39) DMXL2-ORACACAGGCATTGAACATTCTCATT (SEQ ID NO: 40) DMD DMD-OFCCCAAATGCAAACAGTCTCTTCTATT (SEQ ID NO: 41) DMD-IFGCAAACAGTCTCTTCTATTTCTTTCTTTTT (SEQ ID NO: 42) DMD-IRGTGGCAACTGGACATCAGCTTAT (SEQ ID NO: 43) DMD-OR AATTGTCAAGTGACGTGGGAAAGT(SEQ ID NO: 44) MBD3L2 MBD3L2-OF GAGAAGGTTCAAGTCCACTGCATT(SEQ ID NO: 45) MBD3L2-IF TGCATTTGGAGAGCGTCTTAAGTAT (SEQ ID NO: 46)MBD3L2-IR CCAGAGATTCACTGGCCGTC (SEQ ID NO: 47) MBD3L2-ORCTCAGCACCAGCTCTGTCCAG (SEQ ID NO: 48) ITGA2B ITGA2B-OFCTTCCCACAGCCTCCTGTCA (SEQ ID NO: 49) ITGA2B-IF AACCCTCTCAAGGTGGACTGG(SEQ ID NO: 50) ITGA2B-IR CTGCGATCCCGCTTGTGAT (SEQ ID NO: 51) ITGA2B-ORCAGGAAGATCTGTCTGCGATCC (SEQ ID NO: 52) CDH4 CDH4-OFGATGATAATTCTGTTCTCTCCAAAGCA (SEQ ID NO: 53) CDH4-IFGGGTAGTCTCAATTTCTGTCAGTGC (SEQ ID NO: 54) CDH4-IRGAGATTCTGTGTTGATTCTTTTGGTG (SEQ ID NO: 55) CDH4-ORGGTCACGTGTGTCTGGGAGATT (SEQ ID NO: 56) SAT1 SAT1-OFCTTGAATATCTTTCGATAAACAACAAGGT (SEQ ID NO: 57) SAT1-IFGATAAACAACAAGGTGGTGTGATCTTAA (SEQ ID NO: 58) SAT1-IRCACATTTAAATGACTCACGAGAATGAA (SEQ ID NO: 59) SAT1-ORCAAACAGAAACTCTAAGTACCAGTGTGTAC (SEQ ID NO: 60) FTHP1 FTHP1-OFCCCATAGCTGTGGGGTGACTT (SEQ ID NO: 61) FTHP1-IF CAAGGCAGTGCATGCATGTT(SEQ ID NO: 62) FTHP1-IR GGTACAAATCAAAAGAACTTAAGTGGATG (SEQ ID NO: 63)FTHP1-OR TGAAGGAATGGTACAAATCAAAAGAAC (SEQ ID NO: 64) TPT1 TPT1-OFGGATCTATCACCTGTCATCATAACTGG (SEQ ID NO: 65) TPT1-IFATCATAACTGGCTTCTGCTTGTCAT (SEQ ID NO: 66) TPT1-IRGATGACATCAGTCCCATTTGTCTTAA (SEQ ID NO: 67) TPT1-ORATGAAGAGCTCAAGATGACATCAGTC (SEQ ID NO: 68) FTH1 FTH1-OFCCCATTTGTGTGACTTCATTGAGA (SEQ ID NO: 69) FTH1-IFCATTACCTGAATGAGCAGGTGAAA (SEQ ID NO: 70) FTH1-IR GCAAGTTGGTCACGTGGTCA(SEQ ID NO: 71) FTH1-OR GCTCCCATCTTGCGCAAGT (SEQ ID NO: 72) SAT1 SAT1-OFTGGCAATCTCAGATGCAGTTTG (SEQ ID NO: 73) SAT1-IFGGAGAGTCAGATCTTTCTCCTTGAATAT (SEQ ID NO: 74) SAT1-IRTTAAGATCACACCACCTTGTTGTTTATC (SEQ ID NO: 75) SAT1-ORTTCAAATATATTAAGATCACACCACCTTGT (SEQ ID NO: 76) MARCKS MARCKS-OFCGGCAGAGTAAAAGAGCAAGCT (SEQ ID NO: 77) MARCKS-IFGCAAGCTTTTGTGAGATAATCGAA (SEQ ID NO: 78) MARCKS-IR GGCACCACTCCAACAAACAAA(SEQ ID NO: 79) MARCKS-OR CCTGGTTGTAGACAAGTTCTCCAA (SEQ ID NO: 80)PNPLA8 PNPLA8-OF TGGCCAGATGTGCCGTTAG (SEQ ID NO: 81) PNPLA8-IFAGATGTGCCGTTAGAGTGCATAGTAT (SEQ ID NO: 82) PNPLA8-IRCCGTGTTTCTCACATCACTCTCAT (SEQ ID NO: 83) PNPLA8-ORTCAAGCTTGTGTATGTTACCGTGTT (SEQ ID NO: 84) DPM1 DPM1-OFGAGATGATTGTTCGGGCAAGA (SEQ ID NO: 85) DPM1-IF TTCGGGCAAGACAGTTGAATTATA(SEQ ID NO: 86) DPM1-IR TCACCATAAACACGATCCACAAA (SEQ ID NO: 87) DPM1-ORTTCATTTCCTCCCAACTTGGAT (SEQ ID NO: 88) CD7 CD7-OF TGGCGGTGATCTCCTTCCT(SEQ ID NO: 89) CD7-IF GCTGGCGAGGACACAGATAAA (SEQ ID NO: 90) CD7-IRATGCCGCCGAATTCTTATCC (SEQ ID NO: 91) CD7-OR TGTGCGACATGTCCTCGTACA(SEQ ID NO: 92) GPR37 GPR37-OF GAAGTGGCTGCTGGAGGACTT (SEQ ID NO: 93)GPR37-IF CCTGCAAGATCGTGCCCTATA (SEQ ID NO: 94) GPR37-IRTGCACAGAGCACATAAGGTGAA (SEQ ID NO: 95) GPR37-OR CACGGAAGCGGTCTATGCA(SEQ ID NO: 96) PCSK6 PCSK6-OF ACCTCCTGCATCACCAACCA (SEQ ID NO: 97)PCSK6-IF AGCAACGCTGACGAGACATTC (SEQ ID NO: 98) PCSK6-IRCACAGCCGGTTGGACTTCA (SEQ ID NO: 99) PCSK6-OR CGGCAGCAGAACTGAATGAA(SEQ ID NO: 100) TK2 TK2-OF GCCCCTGTTCTGGTGATTGA (SEQ ID NO: 101) TK2-IFCCACCACATGGAGAGGATGTTA (SEQ ID NO: 102) TK2-IR TCCGATTCTCTGGAGTTAATATTCG(SEQ ID NO: 103) TK2-OR AGCCATAGACCTTTTGCCTCCTA (SEQ ID NO: 104) TTBK2TTBK2-OF TAGAAGCCAGGCTACGCAGATATA (SEQ ID NO: 105) TTBK2-IFCCTGGCCCAAATTCTTCAAA (SEQ ID NO: 106) TTBK2-IR CCTGGACTCTTGCACTGAGTAGTG(SEQ ID NO: 107) TTBK2-OR AGATCCTGGACTCTTGCACTGAGT (SEQ ID NO: 108)GLTSCR2 GLTSCR2-OF GACCGGTTCAAGAGCTTCCA (SEQ ID NO: 109) GLTSCR2-IFTTCCAGAGGAGGAATATGATCGA (SEQ ID NO: 110) GLTSCR2-IRTTCTCCACCAGCTTCACCTTGTA (SEQ ID NO: 111) GLTSCR2-ORTGATGGCAGCTACAACTGGATCT (SEQ ID NO: 112) CDKL3 CDKL3-OFTCAGTTTTGGGAGAGGAAATAGAAA (SEQ ID NO: 113) CDKL3-IFTAGAAAAAGAGAAAAAGCCCAAGGA (SEQ ID NO: 114) CDKL3-IRTCTCCTCTTCCTCCTTTGACTTTAAT (SEQ ID NO: 115) CDKL3-ORTCCACCTTCATACTCTTTCTTTTTTG (SEQ ID NO: 116) ZSCAN16 ZSCAN16-OFCTCCTCAGCATCCTAAGTCCAAA (SEQ ID NO: 117) ZSCAN16-IF GGGCAGATCAGAATGGCAA(SEQ ID NO: 118) ZSCAN16-IR CCACATTCATCACATTTATATCGTCTT (SEQ ID NO: 119)ZSCAN16-OR GCTATGACTGAAACTTTTCCCACAT (SEQ ID NO: 120) S100P S100P-OFGCACGCAGACCCTGACCA (SEQ ID NO: 121) S100P-IF GCTGATGGAGAAGGAGCTACCA(SEQ ID NO: 122) S100P-IR TTGAGCAATTTATCCACGGCAT (SEQ ID NO: 123)S100P-OR CGTCCAGGTCCTTGAGCAATT (SEQ ID NO: 124) KRAS KRAS-OFAGACACAAAACAGGCTCAGGACTT (SEQ ID NO: 125) KRAS-IFCAGGCTCAGGACTTAGCAAGAAG (SEQ ID NO: 126) KRAS-IR CACCCTGTCTTGTCTTTGCTGAT(SEQ ID NO: 127) KRAS-OR GGCATCATCAACACCCTGTCT (SEQ ID NO: 128) UTF1UTF1-OF CCCCCGTCGCTGAACAC (SEQ ID NO: 129) UTF1-IF GGCGACATCGCGAACATC(SEQ ID NO: 130) UTF1-IR GCTCCACGTGCTGGTTCAA (SEQ ID NO: 131) UTF1-ORCGGCCAGGGACACTGTCT (SEQ ID NO: 132) ZMIZ2 ZMIZ2-OF GGACCTGCTCCCGGAACT(SEQ ID NO: 133) ZMIZ2-IF GGAACTGACCAACCCTGATGAG (SEQ ID NO: 134)ZMIZ2-IR CAGGTCGTCATTGTTGTTCGTAG (SEQ ID NO: 135) ZMIZ2-ORAACAGAGAAAGCAGGTCGTCATT (SEQ ID NO: 136) DDX3X DDX3X-OFGAGGTGGCTATGGAGGCTTTTA (SEQ ID NO: 137) DDX3X-IFCAACAGTGATGGATATGGAGGAAA (SEQ ID NO: 138) DDX3X-IRGCTCAGTTACCCCACCAGTCAA (SEQ ID NO: 139) DDX3X-OR TGTTTGGCAGGGTGACCTACT(SEQ ID NO: 140) CABLES1 CABLES1-OF GTCCTCAGCCTTGTGGTAGCA(SEQ ID NO: 141) CABLES1-IF TGGTAGCACAAATGAATGCAGTAA (SEQ ID NO: 142)CABLES1-IR GCAGTATTCTGTGAACGCTGGTA (SEQ ID NO: 143) CABLES1-ORCAAGATTTGAGGTTCAGTGCAGTATT (SEQ ID NO: 144) GAPDH GAPDH-OFCATTGCCCTCAACGACCACTT (SEQ ID NO: 145) GAPDH-IF ACCACTTTGTCAAGCTCATTTCCT(SEQ ID NO: 146) GAPDH-IR CACCCTGTTGCTGTAGCCAAAT (SEQ ID NO: 147)GAPDH-OR ATGTGGGCCATGAGGTCCA (SEQ ID NO: 148) OF = Outer forward, IF =Inner forward, IR = Inner reverse, OR = Outer reverse

TABLE 3 qPCR primers of 6 confirmed bacterial  species16S rRNA primer sequences Strain (5′-3′) AtopobiumF: CGAATACTTCGAGACTTCCGCA parvulum (SEQ ID NO: 149)R: CAATCTGGCTGGTCGGTCTC (SEQ ID NO: 150) GranulicatellaF: CAAGCTTCTGCTGATGGATGGA adiacens (SEQ ID NO: 151)R: CTCAGGTCGGCTATGCATCAC (SEQ ID NO: 152) NeisseriaF: CATGCCGCGTGTCTGAAGAA elongata (SEQ ID NO: 153)R: CCGTCAGCAGAAACGGGTATT (SEQ ID NO: 154) PrevotellaF: GACGGCATCCGATATGAAACA nigrescens (SEQ ID NO: 155)R: TGCACGCTACTTGGCTGGT (SEQ ID NO: 156) StreptococcusF: AGAACGCTGAAGGAAGGAGCTT australis (SEQ ID NO: 157)R: CAATAGTTATCCCCCGCTACCA (SEQ ID NO: 158) StreptococcusF: CCGCATAATAGCAGTTRTTGCA mitis (SEQ ID NO: 159)R: ACAACGCAGGTCCATCTGGTA (SEQ ID NO: 160)

TABLE 4 Salivary mRNA up and down-regulated in pancreatic cancerconfirmed by microarray and qPCR GenBank Gene accession symbol Gene nameno. Locus Gene functions up-regulated genes ACRV1 acrosomal vesicleprotein 1 NM_001612 11q23-q24 multicellular organismal developmentCDC14B CDC14 cell division cycle 14 homolog B NM_003671 9q22.33 proteinamino acid dephosphorylation; cell division ASH2L ash2-like NM_0046748p11.2 regulation of transcription STIM2 stromal interaction molecule 2NM_020860 4p15.2 calcium ion binding and transport GPR124 Gprotein-coupled receptor 124 NM_032777 8p12 G-protein coupled receptorprotein signaling pathway; neuropeptide signaling pathway LILRA2leukocyte immunoglobulin-like receptor, NM_006866 19q13.4 defenseresponse; immune response; signal transduction subfamily A, member 2 ENGendoglin NM_000118 9q33-q34.1 transport; cell adhesion; bloodcirculation; organ morphogenesis RBM24 RNA binding motif protein 24NM_153020 6p22.3 type I hypersensitivity LRRK1 leucine-rich repeatkinase 1 NM_024652 15q26.3 protein amino acid phosphorylation; smallGTPase mediated signal transduction DMXL2 Dmx-like 2 NM_015263 15q21.2translational initiation ZSCAN16 zinc finger and SCAN domain containing16 NM_025231 6p22.1 regulation of transcription, DNA-dependent MBD3L2methyl-CpG binding domain protein 3-like 2 NM_144614 19p13.2 opposeMBD2-MeCP1-mediated methylation silencing GPX3 glutathione peroxidase 3NM_002084 5q23 glutathione metabolic process; response to oxidativestress ITGA2B integrin, alpha 2b NM_000419 17q21.32 cell adhesion;integrin-mediated signaling pathway; platelet activation CDH4 cadherin 4NM_001794 20q13.3 homophilic cell adhesion; positive regulation of axonextension S100P S100 calcium binding protein P NM_005980 4p16endothelial cell migration FTHP1 ferritin, heavy polypeptide pseudogene1 NG_005639 6p21.3-p12 ZMIZ2 zinc finger, MIZ-type containing 2NM_031449 7p13 regulation of transcription, DNA-dependent DDX3X DEAD(Asp-Glu-Ala-Asp; SEQ ID NM_001356 Xp11.3-p11.23 embryogenesis;spermatogenesis; cellular growth and NO: 161) box polypeptide 3,X-linked division UTF1 undifferentiated embryonic cell transcriptionNM_003577 10q26 regulation of transcription, DNA-dependent factor 1 KRASv-Ki-ras2 Kirsten rat sarcoma viral oncogene NM_004985 12p12.1 smallGTPase mediated signal transduction; Ras protein homolog signaltransduction DMD dystrophin NM_000109 Xp21.2 cytoskeletal anchoring atplasma membrane; peptide biosynthetic process CABLES1 Cdk5 and Ablenzyme substrate 1 NM_001100619 18q11.2 regulation of cell division;regulation of cell cycle Down-regulated genes TPT1 tumor protein,translationally-controlled 1 NM_003295 13q12-q14 cellular calcium ionhomeostasis; anti-apoptosis regulation of apoptosis MARCKS myristoylatedalanine-rich protein kinase C NM_002356 6q22.2 cell motility;phagocytosis; membrane trafficking; substrate mitogenesis SAT1spermidine/spermine N1-acetyltransferase 1 NM_002970 Xp22.1 metabolicprocess PNPLA8 patatin-like phospholipase domain NM_015723 7q31 lipidmetabolic process; modulates cellular growth containing 8 programs;inflammation; ion channel function DPM1 dolichyl-phosphatemannosyltransferase NM_003859 20q13.13 GPI anchor biosynthetic process;protein amino acid polypeptide 1 mannosylation CD7 CD7 moleculeNM_006137 17q25.2-q25.3 immune response; tyrosine kinase signalingpathway; T cell activation PCSK6 proprotein convertase subtilisin/kexintype 6 NM_138319 15q26.3 tumor progression; proteolysis; regulation ofBMP signaling pathway TK2 thymidine kinase 2 NM_004614 16q22-q23.1nucleotide and nucleic acid metabolic process; DNA replication FTH1ferritin, heavy polypeptide 1 NM_002032 11q13 iron ion transport, immuneresponse, negative regulation of cell proliferation, oxidation reductionTUBA1A tubulin, alpha 1b NM_006082 12q13.12 microtubule-based process,protein polymerization GLTSCR2 glioma tumor suppressor candidate regionNM_015710 19q13.3 tumor suppressive activity gene 2 CDKL3cyclin-dependent kinase-like 3 NM_016508 5q31 protein modificationprocess; protein amino acid phosphorylation The human Genome U133 Plus2.0 microarrays were used to identify the difference in RNA expressionpatterns in saliva from 12 pancreatic cancer patients and 12 healthycontrols. Using a criteria of a change in regulation >4-fold in all 12pancreatic cancer saliva specimens and a cutoff of p-value <0.01 inmicroarray study we identified 49 up-regulated and 21 down-regulatedtranscripts. These transcripts were subjected to qPCR verification.Using a cutoff of p-value <0.05 in the qPCR verification study, weidentified 35 mRNAs, showing significant up-regulation (23 mRNAs) andsignificant down-regulation (12 mRNAs) in pancreatic cancer saliva.

TABLE 5 Cross-disease comparison of microarray profiles of 12 validatedmRNA biomarkers Pancreatic Gene symbol cancer Oral cancer Lung cancerBreast cancer MBD3L2 0.011 0.391 0.770 0.419 KRAS <0.001 0.248 0.3460.906 STIM2 0.013 0.160 0.479 0.963 DMXL2 0.009 0.869 0.056 0.226 ACRV10.004 0.946 0.304 0.397 DMD 0.008 0.633 0.979 0.558 CABLES1 0.002 0.5740.096 0.473 TK2 0.014 0.966 0.007 0.311 GLTSCR2 0.006 0.417 0.336 0.073CDKL3 <0.001 0.107 0.227 0.190 TPT1 0.007 0.213 0.331 0.422 DPM1 0.0050.135 0.082 0.428 Cancer specificity of the twelve validated mRNAbiomarkers were evaluated across different microarray discovery studiesthat has been performed in our laboratory on diverse cancers, includingpancreatic cancer (HG U133 Plus 2.0), oral cancer (HG U133A), breastcancer (HG U133 Plus 2.0) and lung cancer (HG U133 Plus 2.0). T-testp-values were calculated for each transcript between cancers and healthycontrols in different microarray studies. Except TK2 that also showedsignificant variation in lung cancer microarray study (P < 0.05), therest mRNAs/transcripts that showed significant variations in pancreaticcancer study were not significantly altered in other cancer microarraystudies.

TABLE 6 Effect of age and smoking on the validated biomarkers PancreaticHealthy Chronic cancer control pancreatitis Biomarker age smoking agesmoking age smoking ACRV1 0.308 0.228 0.899 0.187 0.909 0.372 STIM20.628 0.684 0.352 0.669 0.855 0.130 DMXL2 0.674 0.621 0.158 0.869 0.2260.264 CABLES1 0.398 0.370 0.489 0.154 0.829 0.314 DMD 0.599 0.540 0.2810.097 0.663 0.234 MBD3L2 0.535 0.201 0.366 0.078 0.396 0.601 DPM1 0.5500.617 0.345 0.177 0.729 0.732 TK2 0.977 0.673 0.721 0.125 0.705 0.946GLTSCR2 0.153 0.199 0.687 0.361 0.883 0.207 CDKL3 0.507 0.936 0.8110.182 0.712 0.538 TPT1 0.441 0.442 0.394 0.206 0.728 0.719 KRAS 0.4610.380 0.776 0.880 0.845 0.820 G. adiacens 0.306 0.509 0.087 0.143 0.3700.194 N. elongata 0.987 0.053 0.280 0.678 0.306 0.030 S. mitis 0.5840.042 0.433 0.974 0.779 0.217 Effect of age and smoking history wascalculated for the validated biomarkers. The effect was consideredsignificant if p-value <0.05 (shown in bold). Gene names are listed inTable A4. We further evaluated the effect of these two factors on themodel building (N. elongate and S. mitis). Neither of them contributessignificantly to the models using microbial biomarkers after discountingthe group difference.

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Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, one of skill in the art will appreciate that certainchanges and modifications may be practiced within the scope of theappended claims. In addition, each reference provided herein isincorporated by reference in its entirety to the same extent as if eachreference was individually incorporated by reference. Where a conflictexists between the instant application and a reference provided herein,the instant application shall dominate.

1. A method for distinguishing subjects with pancreatic cancer fromthose without pancreatic cancer, the method comprising detecting a levelof a combination of markers in a saliva sample from a test subject withan assay that specifically detects a marker selected from the groupconsisting of a nucleic acid or polypeptide encoded by a nucleic acidlisted in FIG. 4 wherein an increase in the level of MBD3L2, KRAS,STIM2, DMXL2, ACRV1, DMD and CABLES1 and a decrease in the level of TK2,GLTSCR2, CDKL3, TPT1 and DPM1 relative to a control distinguishes thetest subject from those subjects without pancreatic cancer.
 2. Themethod of claim 1, wherein the assay detects more than one marker. 3.(canceled)
 4. (canceled)
 5. (canceled)
 6. (canceled)
 7. (canceled) 8.(canceled)
 9. (canceled)
 10. (canceled)
 11. (canceled)
 12. (canceled)13. The method of claim 1, wherein the combination of markers is KRAS,MBD3L2, ACRV1, and DPM1, and wherein higher levels of KRAS, MBD3L2, andACRV1, and a lower level of DPM1 relative to a control distinguishes thetest subject from those subjects without pancreatic cancer and all ofthese markers are detected.
 14. (canceled)
 15. The method of claim 1wherein those subjects with pancreatic cancer are distinguished fromthose subjects without pancreatic cancer but with chronic pancreatitis,the method comprising detecting a level of a combination of markerswherein the markers are MBD3L2, KRAS, STIM2, ACRV1, DMD, CABLES1, TK2,GLTSCR2, and CDKL3.
 16. A method for distinguishing subjects withpancreatic cancer from those without, the method comprising detecting alevel of a combination of microbes in a saliva sample from a testsubject wherein the microbes are selected from the group of microbes G.adiacens, N. elongata and S. mitis; and wherein an increase in the levelof G. adiacens and a decrease in the level of N. elongata and S. mitisin the test subject relative to a control distinguishes the test subjectfrom those subjects without pancreatic cancer.
 17. The method of claim16, wherein more than one microbe from the group of microbes isdetected.
 18. (canceled)
 19. (canceled)
 20. (canceled)
 21. (canceled)22. (canceled)
 23. (canceled)
 24. (canceled)
 25. (canceled) 26.(canceled)
 27. The method of claim 16, wherein the analyzing stepcomprises analyzing the saliva sample from the subject with an assaythat specifically detects Neisseria elongate and Streptococcus mitis orGranulicatella adiacens and Streptococcus mitis, and wherein thecomparing step comprises determining whether or not the levels ofNeisseria elongate and Streptococcus mitis or Granulicatella adiacensand Streptococcus mitis have increased or decreased in the samplerelative to a control.
 28. The method of claim 16, wherein the markerdistinguishes between chronic pancreatitis and pancreatic cancer. 29.The method of claim 28, wherein the markers are Granulicatella adiacensand Streptococcus mitis and they are both detected.
 30. (canceled)
 31. Akit, wherein the kit comprises reagents that specifically detect themarkers KRAS, MBD3L2, ARV1, and DPM1.
 32. (canceled)
 33. A kit, whereinthe kit comprises reagents that specifically detect the markersGranulicatella adiacens and Streptococcus mitis.
 34. The method of claim1, wherein the combination of markers is KRAS, MBD3L2 and CDKL3 andwherein higher levels of KRAS and MBD3L2 and a lower level of CDKL3relative to a control distinguishes the test subject from those subjectswithout pancreatic cancer and all of these markers are detected.
 35. Themethod of claim 34 further comprising the detection of ACRV1.